R3(References on References on References) on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(44)
R3(0) on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari
https://qiita.com/kaizen_nagoya/items/a8eac9afbf16d2188901
What are the most important statistical ideas of the past 50 years?
Andrew Gelman, Aki Vehtari
https://arxiv.org/abs/2012.00174
References 44
Efron, B., and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. London: Chapman and Hall.
http://www.ru.ac.bd/stat/wp-content/uploads/sites/25/2019/03/501_02_Efron_Introduction-to-the-Bootstrap.pdf
REFERENCE ON 44
44.1
Abramovitch, L. and Singh, K. (1985) Edgeworth corrected pivotal statistics and the bootstrap. Ann. Statist. 13, 116-132.
https://projecteuclid.org/journalArticle/Download?urlId=10.1214%2Faos%2F1176346580
REFERENCE ON 44.1
44.1.1
BABU, G. J. and SINGH, K. (1982). Edgeworth expansions for sampling without replacement from finite populations. J. Multivariate Anal., to appear.
44.1.2
BABU, G. J. AGE SINGH, K. (1983). Inference on means using the bootstrap. Ann. Statist. 11, 999-1003.
44.1.3
BHATTACHARYA, R. N. and GHOSH, J. K. (1978). On the validity of formal Edgeworth expansion. Arm. Statist. 6 434-451.
44.1.4
EFRON, B. (1979). Bootstrap methods: Another look at the jackknife. Arm. Statist. 71-26.
44.1.5
HALL, P. (1983). Inverting an Edgeworth expansion. Arm. Statist. 11 569-576.
44.1.6
JOHNSON, N. J. (1978). Modified t-tests and confidence intervals for asymmetrical population. J. Amer. Statist. Assoc. 73 536-544.
44.1.7
SINGH, K. (1981). On asymptotic accuracy of Efron's bootstrap. Arm. Statist. 9 1187-1195.
44.1.8
WITHERS, C. S. (1983). Expansion for the distribution and guantilea of a regular functional of the empirical distribution with applications to non-parametric confidence intervals. Arm. Statist. 11 577-587.
44.2
Akaike, H. (1973) Information theory and an extension of the maxi- mum likelihood principle. In Second International Symposium on In- formation Theory, (eds. B.N. Petrov and F. Czaki) Akademiai Kiad6, Budapest, 267-81.
44.3
Allen, D.M. (1974) The relationship between variable selection and data augmentation and a method of prediction. Technometrics 16, 125-7.
44.4
Anderson, T.W. (1958) An introduction to multivariate statistical analysis. Wiley, New York.
44.5
Bai, C., Bickel, P.J., and Olshen, R.A. (1990) Hyperaccuracy of boot-
strap based prediction. Probability in Banach Spaces VII Proceed- ings of the Seventh International Conference (edited by E. Eberlein, J. Kuelbs, and M.B. Marcus), Birkhauser Boston, Cambridge, Mas- sachusetts, 31-42.
44.6
Bai. C, and Olshen, R.A. (1988) Discussion of "Theoretical comparison of bootstrap confidence intervals" by P. Hall, Ann. Statist. 16, 953- 956.
44.7
Barnard, G.A. (1963) Contribution to discussion. J. Royal. Statist. Soc. B 25, 294.
44.8
Barndorff-Neilson, O.E. and Cox, D.R. (1989) Asymptotic techniques for use in statistics. Chapman and Hall, London, New York.
44.9
Becker, R., Chambers, J. and Wilks. A. (1988) The S language. Wadsworth, Belmont CA.
44.10
Behrens, B.-U. (1929) Ein Beitrag zur Fehlen-Berechnung bei wenigen Beobachtungen. Landwirtsch. Jb. 68, 807-837.
44.11
Beran, R. (1984) Bootstrap methods in statistics. Jber. d. Dt. Math. Verein. 86, 14-30.
44.12
Beran, R. (1987) Prepivoting to reduce level error of confidence sets. Biometrika 74, 457-468.
44.13
Beran, R. (1988) Prepivoting test statistics: a bootstrap view of asymp- totic refinements, J. Amer. Statist. Assoc. 83, 687-697.
44.14
Beran, R. and Ducharme, G. (1991) Asymptotic theory for bootstrap methods in statistics. Centre de Reserches Mathematiques, Univ. of Montreal.
44.15
Beran, R. and Millar, P.W. (1987) Stochastic estimation and testing.
Ann. Statist. 15, 1131-1154.
44.16
Beran, R. and Srivastava, M.S. (1985) Bootstrap tests and confidence
regions for functions of a covariance matrix. Ann. Statist. 13, 95-115.
44.17
Besag, J. and Clifford, P. (1989) Generalized Monte Carlo significance
tests. Biometrika 76, 633-642.
44.18
Bickel, P.J. and Freedman, D.A. (1981) Some asymptotic theory for the
bootstrap. Ann. Statist. 9, 1196-1217.
44.19
Bickel, P.J. and Freedman, D.A. (1983) Bootstrapping regression mod-
els with many parameters, A Festschrift for Erich L. Lehmann, P.J.
44.20
Bickel, K. Doksum and J.L. Hodges eds., Wadsworth, Belmont, CA.
44.21
Bickel, P.J. and Freedman, D.A. (1984) Asymptotic normality and the
bootstrap in stratified sampling, Ann. Statist. 12, 470-482.
44.22
Boos, D. and Monahan, J. (1986) Bootstrap methods using prior infor-
mation. Biometrika 73, 77-83
44.23
Box, G.E. and Jenkihs, G.M (1970) Times series analysis, forecasting,
and control. Holden-Day, San Francisco.
44.24
Breiman, L. (1992) The little bootstrap and other methods for dimen-
sionality selection in regression: X-fixed prediction error. J. Amer.
Statist. Assoc. 87, 738-754.
44.25
Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984) Classifi-
cation and Regression Trees. Wadsworth, Belmont, CA.
44.26
Breiman, L. and Spector, P. (1992) Submodel selection and evaluation
in Regression. The X-Random Case. Int. Stat. Rev. 60, 291-319.
44.27
Buckland, S.T. (1983) Monte Carlo methods for confidence interval es- timation using the bootstrap technique. Bull. Appl. Statist. 10, 194-212.
44.28
Buckland, S.T. (1984) Monte Carlo confidence intervals. Biometrics 40,811-817.
44.29
Buckland, S.T. (1985) Calculation of Monte Carlo confidence intervals.
Appl. Statist. 34, 296-301.
44.30
Carlstein, E. (1986) The use of subseries values for estimating the vari-
ance of a general statistic from a stationary sequence. Ann. Statist.
14, 1171-1179.
44.31
Chambers, J. and Hastie. T.J. (eds) (1991) Statistical models in S.
Wadsworth, Belmont, CA.
44.32
Chatfield, C. (1980) The analysis of time series: an introduction (second
edition). Chapman and Hall, London.
44.33
Cleveland, W.S. (1979) Robust locally-weighted regression and smooth-
ing scatterplots. J. Amer. Statist. Assoc. 74, 829-36.
44.34
Cox, D.R. and Hinkley, D.V. (1974) Theoretical Statistics. Chapman and Hall, London.
44.35
Cox, D.R. and Reid, N. (1987) Orthogonal parameters and approximate conditional inference (with discussion) J. Royal. Statist. Soc. B 49, 1-39.
44.36
Cox, D. R. and Snell. E.J. (1981) Applied statistics. Principles and ex- amples. Chapman and Hall, London.
44.37
Crawford, S. (1989) Extensions to the CART algorithm. Int. J. of Ma- chine Studies 31, 197-217.
44.38
Davison, A.C. (1988) Discussion of papers by D.V. Hinkley and by T.J. DiCiccio and J.P. Romano. J. Royal. Statist. Soc. B 50, 356-357.
44.39
Davison A.C. and Hinkley, D.V. (1988) Saddlepoint approximations in resampling methods. Biometrika 75, 417-431.
44.40
Davison A.C., Hinkley, D.V. and Schechtman, E. (1986) and Efficient
bootstrap simulations, Biometrika 73. 555-566.
44.41
Davison A.C., Hinkley, D.V. and Worton, B.J. (1992) Bootstrap likeli-
hoods, Biometrika 19, 113-130.
44.42
Devroye, L. (1986) Non-uniform random variate generation. Springer,
Berlin, New York.
44.43
Diaconis, P. and Efron, B. (1983) Computer-intensive methods in statis-
tics. Sci. Amer. 248, 116-130.
44.44
DiCiccio, T.J. Hall, P. and Romano, J.P. (1989b) Comparison of para-
metric and empirical likelihood functions. Biometrika 16, 465-476.
44.45
DiCiccio, T.J., Martin, M. and Young, A. (1992) Analytic approxima- tions to bootstrap distribution functions using saddlepoint methods. Tech. report, Stanford University.
44.46
DiCiccio, T .J. and Romano, J.P . (1988) A review of bootstrap confidence
intervals (with discussion). J. Royal. Statist. Soc. B 50, 338-370.
44.47
DiCiccio, T.J. and Romano, J.P. (1989) The automatic percentile method: Accurate confidence limits in parametric models, Can. J. Statist. 17, 155-169.
44.48
DiCiccio, T.J. and Romano, J.P. (1990) Nonparametric confidence limits
by resampling methods and least favorable families, Inter. Statist.
Review 58, 59-76.
44.49
DiCiccio, T.J. and Tibshirani, R. (1987) Bootstrap confidence intervals
and bootstrap approximations. J. Amer. Statist. Assoc. 82, 163-170.
44.50
Diggle, P. (1990) Time Series, a Biostatistical Introduction. Clarendon
Press, Oxford.
44.51
Do, K.A. and Hall, P. (1991) On importance resampling for the boot-
strap, Biometrika 78, 161-167.
44.52
Draper, N. and Smith, H. (1981) Applied regression analysis, (second
edition). Wiley, New York.
44.53
Ducharme, G.R., Jhun, M., Romano, J.P., and Truong, K.N. (1985)
Bootstrap confidence cones for directional data. Biometrika 72, 637-645.
44.54
Edgington, E.S. (1987) Randomization Tests, 2nd ed. Dekker, New York.
44.55
Efron, B. (1979a) Bootstrap methods: another look at the jackknife. Ann. Statist. 7, 1-26.
44.56
Efron, B. (1979b) Computers and the theory of statistics: thinking the unthinkable. SIAM Review 21, 46Q-480.
44.57
Efron, B. (1981a) Nonparametric standard errors and confidence inter- vals. (With discussion.) Can. J. Statist. 9, 139-172.
44.58
Efron, B. (1981b) Nonparametric estimates of standard error: the jack- knife, the bootstrap, and other methods. Biometrika 68, 589-599.
44.59
Efron, B. (1982) The jackknife, the bootstrap and other resampling
plans. Volume 38 of GEMS-NSF Regional Conference Series in Applied Mathematics. SIAM.
44.60
Efron, B. (1983) Estimating the error rate of a prediction rule: improve-
ments on cross-validation. J. Amer. Statist. Assoc. 78, 316-331.
44.61
Efron, B. (1985) Bootstrap confidence intervals for a class of parametric
problems. Biometrika 72, 45-58.
44.62
Efron, B. (1986) How biased is the apparent error rate of a prediction
rule? J. Amer. Statist. Assoc. 81, 461-70.
44.63
Efron, B. (1987) Better bootstrap confidence intervals. (with discussion.)
J. Amer. Statist. Assoc. 82, 171-200.
44.64
Efron, B. (1988) Bootstrap confidence intervals: good or bad? (With
discussion.) Psychol. Bull. 104, 293-296.
44.65
Efron, B. (1990) More efficient bootstrap computations. J. Amer.
Statist. Assoc. 85, 79-89.
44.66
Efron, B. (1991) Regression percentiles using asymmetric squared error
loss. Statistica Sinica 1, 93-125.
44.67
Efron, B. (1992a). Six questions raised by the bootstrap. Exploring the
Limits of Bootstrap (Eds. R. LePage and L. Billard), John Wiley and
Sons, 99-126, New York.
44.68
Efron, B. (1992b) Jacknife-after-bootstrap standard errors and influence
functions. J. Royal. Statist. Soc. B 54, 83-127.
44.69
Efron, B. (1992c) Bayes and likelihood calculations from confidence in-
tervals. Tech, rep., Dept. of Statistics, Stanford Univ.
44.70
Efron, B. and Feldman, D. (1991) Compliance as an explanatory variable
in clinical trials. J. Amer. Statist. Assoc. 86, 9-26.
44.71
Efron, B. and Gong, G. (1983) A leisurely look at the bootstrap, the
jackknife and cross-validation. Amer. Statistician 37, 36-48.
44.72
Efron, B. and Stein, C. (1981) The jackknife estimate of variance, Ann.
Statist. 9, 586-596.
44.73
Efron, B. and Tibshirani, R. (1985) The bootstrap method for assessing
statistical accuracy. Behaviormetrika 17, 1-35.
44.74
Efron, B. and Tibshirani, R. (1986) Bootstrap measures for standard
errors, confidence intervals, and other measures of statistical accuracy.
Statistical Science 1, 54-77.
44.75
Efron, B. and Tibshirani, R. (1991) Statistical data analysis in the
computer age. Science 253, 390-395.
44.76
Eubank, R.L. (1988) Smoothing Splines and Nonparametric Regression. Marcel Dekker, New York and Basel.
44.77
Faraway, J. and Jhun, M. (1990) Bootstrap choice of bandwidth for density estimation. J. Amer. Statist. Assoc. 85, 1119-1122.
44.78
Fernholz, L.T. (1983). Von Mises calculus for statistical functionals. Lec- ture Notes in Statistics, 19, Springer, New York.
44.79
Feuerverger, A. (1989) On the empirical saddlepoint approximation. Biometrika 76, 457-464.
44.80
Fisher, N.l. and Hall, P. (1989) Bootstrap confidence regions for direc- tional data. J. Amer. Statist. Assoc. 84, 996-1002.
Fisher, N.l. and Hall, P. (1990) On bootstrap hypothesis testing. Austral. J. Statist. 32, 177-190.
Freedman, D.A. (1981) Bootstrapping regression models. Ann. Statist. 9, 1218-1228.
Freedman, D.A. and Peters, S.C. (1984) Bootstrapping a regression equation: Some empirical results. J. Amer. Statist. Assoc. 79, 97- 106.
Giampaolo, C., Gray, A., Olshen, R. and Szabo, S. (1988) Predicting induced duodenal ulcer and adrenal necrosis with classification trees. Technical Report 125, Division of Biostatistics, Stanford University.
Gine, E. and Zinn, J. (1989) Necessary conditions for the bootstrap of the mean, Ann. Statist. 17, 684-691.
Gine, E. and Zinn, J. (1990) Bootstrap general empirical measures, Ann. Prob. 18, 851-869.
Gleason, J.R. (1988) Algorithms for balanced bootstrap simulations, Amer. Statist. 42, 263-266.
Godambe, V.P. (1960) An optimum property of regular maximum like- lihood estimation. Ann. Math. Statist. 31, 1208-1211.
Godambe, V.P. (ed.) (1991) Estimating functions. Clarendon Press, Ox- ford.
Godambe, V.P. and Thompson, M.W. (1984) Robust estimation through estimating equations. Biometrika 71, 115-125.
Golub, G., Heath, M., and Wahba, G. (1979) Generalized cross valida- tion as a method for choosing a good ridge parameter. Technometrics 21, 215-224.
Golub, G. and Van Loan, C. V. (1983) Matrix computations. Johns Hopkins University Press.
Graham, R.L., Hinkley, D.V., John, P.W.M. and Shi, S. (1990) Balanced design of bootstrap simulations. J. Royal. Statist. Soc. B 52, 185-202. Gray, H.L. and Schucany, W.R. (1972) The Generalized Jackknife Statis-
tics, Marcel Dekker, New York.
Gross, S. (1980) Median estimation in sample surveys. Proc. Sect. Survey
Res. Methods, Amer. Stat. Assoc., 181-184.
Hall, P. (1986a) On the bootstrap and confidence intervals. Ann. Statist.
14, 1431-1452.
Hall, P. (1986b) On the number of bootstrap simulations required to construct a confidence interval. Ann. Statist. 14, 1453-1462.
Hall, P. (1987) On the bootstrap and likelihood-based confidence inter- vals. Biometrika 74, 481-493.
Hall, P. (1988a) Theoretical comparison of bootstrap confidence inter- vals. (with discussion.) Ann. Statist. 16, 927-953.
Hall, P. (1988b) On symmetric bootstrap confidence intervals. J. Royal. Statist. Soc. B 50, 35-45.
Hall, P. (1989a) On efficient bootstrap simulation. Biometrika 76, 613- 617.
Hall, P. (1989b) Antithetic resampling for the bootstrap. Biometrika 76, 713-724.
Hall, P. (1990) Performance of bootstrap balanced resampling in dis- tribution function and quantile problems. Prob. Th. Rel. Fields 85, 239-267.
Hall, P. (1991) Bahadur representations for uniform resampling and importance resampling, with applications to asymptotic relative ef- ficiency. Ann. Statist. 19, 1062-1072.
Hall, P. (1992) The Bootstrap and Edgeworth Expansion. Springer- Verlag, New York, Berlin, Heidelberg, London, Paris, Toyko, Hong Kong, Barcelona, Budapest.
Hall, P., DiCiccio, T.J. and Romano, J.P. (1989) On smoothing and the bootstrap. Ann. Statist. 17, 692-704.
Hall, P. and La Scala, B. (1990) Methodology and algorithms of empir- ical likelihood. Internat. Statist. Rev. 58, 109-127.
Hall, P. and Martin, M.A. (1988) On bootstrap resampling and iteration. Biometrika 75, 661-671.
Hall, P. and Titterington, D. (1987) Common structure of techniques for choosing smoothing parameters in regression problems. J. Royal. Statist. Soc. B 49, 184-198.
Hall, P. and Titterington, M. (1988) On confidence bands in nonpara- metric density estimation and regression. J. Mult. Anal. 27, 228-254. Hall, P. and Titterington, M. (1989) The effect of simulation order on level accuracy and power of Monte Carlo tests. J. Royal. Statist. Soc.
B 51, 459-467.
Hall, P. and Wilson, S.R. (1991) Two guidelines for bootstrap hypothesis
testing. Biometrics 47, 757-762.
Hammersley, J.M. and Handscomb, D.C. (1964) Monte Carlo Methods.
Methuen, London.
Hampel, F.R. (1974) The influence curve and its role in robust estima-
tion. J. Amer. Statist. Assoc. 69, 383-393.
Hampel, F.R., Ronchetti, E. M., Rousseeuw, P.J. and Stahel, W. A.
(1986) Robust statistics: The approach based on influence functions. Wiley, New York.
Hardie, W. (1990) Applied Non-parametric Regression. Oxford Univer- sity Press.
Hardie, W. and Bowman, A. (1988) Bootstrapping in nonparametric regression: local adaptive smoothing and confidence bands. J. Amer. Statist. Assoc. 83, 102-110
Hardie, W., Hall, P., and Marron, S. (1988) How far are automatically chosen smoothing parameters from their optimum? J. Amer. Statist. Assoc. 83, 86-95, Rejoinder 100-101.
Hartigan, J.A. (1969) Using subsample values as typical values. J. Amer. Statist. Assoc. 64, 1303-1317.
Hartigan, J.A. (1971) Error analysis by replaced samples. J. Royal. Statist. Soc. B 33, 98-110.
Hartigan, J.A. (1975) Necessary and sufficient conditions for asymptotic joint normality of a statistic and its subsample values. Ann. Statist. 3, 573-580.
Hartigan, J .A. (1986) Discussion of Efron and Tibshirani (1986). Statis- tical Science 1, 75-77.
Hastie, T. and Tibshirani, R. (1990) Generalized additive models. Chap- man and Hall, London.
Hesterberg, T. (1988) Advances in importance sampling. Ph.D. disser- tation, Dept. of Statistics, Stanford University.
Hesterberg, T. (1992) Efficient bootstrap simulations 1: Importance sam- pling and control variates. Unpublished.
Hinkley, D.V. (1977) Jackknifing in unbalanced situations. Technometics 19, 285-292.
Hinkley, D.V. (1988) Bootstrap methods (with discussion). J. Royal. Statist. Soc. B 50, 321-337.
Hinkley, D.V. (1989) Bootstrap significance tests, Proceedings of the 47th session of International Statistical Institute. Paris, 65-74.
Hinkley, D.V. and Shi, S. (1989) Importance sampling and the nested bootstrap. Biometrika 76, 435-446.
Hinkley, D.V. and Wei, B.C. (1984) Improvement ofjackknife confidence limit methods. Biometrika 11, 331-339.
Huber, P. J. (1981) Robust Statistics. Wiley, New York.
Hope, A.C.A. (1968) A simple Monte Carlo test procedure. J. Royal.
Statist. Soc. B 30, 582-598.
lzenman, A.J. and Sommer, C.J. (1988) Philatelic mixtures and multi-
modal densities. J. Amer. Statist. Assoc. 83, 941-953.
Jaeckel, L. (1972) The infinitesimal jackknife. Memorandum, MM 72-
1215-11, Bell Lab. Murray Hill, N.J.
Jeffreys, H. (1961) Theory of probability, 3rd Edition. Oxford, Clarendon
Press.
Johns, M.V. Jr. (1988) Importance sampling for bootstrap. confidence
intervals. J. Amer. Statist. Assoc. 83, 709-714.
Kendall, M.G. and Stuart, A. (1977) The Advanced Theory of Statistics, 4th Edition. Griffin, London.
Kent, T. J. (1982) Robust Properties of Likelihood Ratio Tests.
Biometrika 69, 19~27.
Kiefer, J. and Wolfowitz, J. (1956) Consistency of the maximum likeli-
hood estimator in the presence of infinitely many incidental parame-
ters. Ann. Math. Statist. 27, 887-906,
Kish, L. and Frankel, M.R. (1974) Inference from complex samples (with
discussion). J. Royal. Statist. Soc. B 36, 1~37.
Knuth, D. (1969) The art of computer programming. Addison-Wesley,
Reading, Mass.
Knuth, D. (1986) The TF;X book. Addison-Wesley, Reading, Mass. Kolaczyk, E. (1993) Empirical likelihood for generalized linear models.
To appear, Statist. Sinica.
Konishi, S. (1991) Normalizing transformations and bootstrap confi-
dence intervals, Ann. Statist. 19, 2209~2225.
Krewski, D. and Rao, J.N.K. (1981) Inference from stratified samples:
properties of the linearization, jackknife and balanced repeated repli-
cation methods, Ann. Statist. 9, 1010~1019.
Kiinsch, H. (1989) The jackknife and the bootstrap for general stationary
observations. Ann. Statist. 17, 1217-1241.
Laird, N. and Louis, T.A. (1987) Empirical Bayesian confidence intervals
based on bootstrap sampling. J. Amer. Statist. Assoc. 82, 739~750. Lamport, L. (1986) 11\TEX: a document preparation system. Addison-
Wesley. Reading, Mass.
Leger, C., Politis, D. and Romano, J. (1992) Bootstrap technology and
applications. Technometrics 34, 378~398.
Lehmann, E.L. (1983) Theory of Point Estimation . Wiley, New York. Lele, S. (1991) Resampling using estimating equations. In Estimating
functions, 295~304. V. Godambe ed. Clarendon Press, Oxford.
Lin, D. Y. and Wei, L. J. (1989) Robust Inference for the Cox Propor-
tional Hazards Model. J. Amer. Statist. Ass. 84, 1074~8.
Lindley, D.V. (1958) Fiducial distributions and Bayes theorem. J. Royal.
Statist. Soc. B 20, 102~107.
Linhart, H. and Zucchini, W. (1986) Model selection. Wiley, New York. Liu, R. Y. and Singh, K. (1992) Moving blocks jackknife and bootstrap
capture weak dependence. In Exploring the limits of bootstrap, ed. by
LePage and Billard. John Wiley, New York.
Lob, W.-Y. (1987) Calibrating confidence coefficients. J. Amer. Statist.
Assoc. 82, 155~162.
Loh, W.-Y. (1991) Bootstrap calibration for confidence interval con-
struction and selection, Statist. Sinica 1, 479~495.
Lunneborg, C.E. (1985) Estimating the correlation coefficient: The boot-
strap approach. Psychol. Bull. 98, 209~215.
Mallows, C. (1973) Some comments on Cp. Technometrics 15, 661~675.
Mardia, K.V ., Kent, J.T . and Bibby, J.M. (1979) Multivariate Analysis. Academic Press.
Martin, M.A. (1990) On bootstrap iteration for converge correction in confidence intervals. J. Amer. Statist. Assoc. 85, 1105-1108.
Marriot, F.H.C. (1979) Barnard's Monte Carlo test: how many simula- tions? Appl. Statist. 28, 75-77.
McCarthy, P.J. (1969) Pseudo-replication: half samples. Rev. Internat. Statist. Inst. 37, 239-264.
McKay, M.D. Beckman, R.M. and Conover, W.J. (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239-245.
Miller, R.G. (1964) A trustworthy jackknife. Ann. Math. Statist. 39, 1594-1605.
Miller, R.G. (1974) The jackknife- a review. Biometrika 61, 1-17. Morrison, D.F. (1976) Multivariate statistical methods, 2nd Ed.
McGraw-Hill, New York.
Newton, M., and Raftery, A. (1993) Approximate Bayesian inference
via the weighted likelihood bootstrap (with discussion) To appear, J.
Royal. Statist. Soc. B.
Noreen, E.W. (1989) Computer Intensive Methods for Testing Hypothe-
ses: An Introduction. Wiley, New York.
Ogbonmwan, S.M. and Wynn, H.P. (1988) Resampling generated likeli-
hoods. Statistical Decision Theory and Related Topics IV. S.S. Gupta
and J.O. Berger, eds.), Springer-Verlag, New York, pp. 133-147. Oldford, R.W . (1985) Bootstrapping "\JY Monte Carlo versus approximat- ing the estimator and bootstrapping exactly: Cost and performance.
Comm. Statist. Ser. B 14, 395-242.
Owen, A.B. (1988) Empirical likelihood ratio confidence intervals for a
single functional. Biometrika 75, 237-249.
Owen, A.B. (1990) Empirical likelihood confidence regions. Ann. Statist.
18, 90-120.
Parr, W.C. (1983) A note on the jackknife, the bootstrap and delta
method estimators of bias and variance. Biometrika 70, 719-722. Parr, W.C. (1985) Jackknifing differentiable statistical functionals. J.
Royal. Statist. Soc. B 47, 56-66.
Peters, S.C. and Freedman, D.A. (1984a) Bootstrapping an econometric
model: Some empirical results. J. Bus. Econ. Studies 2, 150-158. Peters, S.C. and Freedman, D.A. (1984b) Some notes on the bootstrap
in regression problems. J. Bus. Econ. Studies 2, 406-409.
Peters, S.C. and Freedman, D.A. (1987) Balm for bootstrap confidence
intervals. J. Amer. Statist. Assoc. 82, 186-187.
Politis, D. and Romano, J. (1992) The stationary bootstrap. Unpub-
lished.
Qin, J. and Lawless, J. (1993) Empirical likelihood and general estimat-
ing equations. To appear, Ann. Statist.
Quenouille, M. (1949) Approximate tests of correlation in time series. J. Royal. Statist. Soc. B 11, 18-44.
Rao, J.N.K. and Wu, C.F.J. (1985) Inference from stratified samples: second-order analysis of three methods for nonlinear statistics. J. Amer. Statist. Assoc. 80, 62Q--630.
Rao, J.N.K. and Wu, C.F.J. (1988) Resampling inference with complex survey data. J. Amer. Statist. Assoc. 83, 231-241.
Rasmussen, J. (1987) Estimating correlation coefficients: bootstrap and parametric approaches. Psych. Bull. 101, 136-139.
Reeds, J.A. (1978) Jackknifing maximum likelihood estimates. Ann. Statist. 6, 727-739.
Reid, N. (1988) Saddlepoint methods and statistical inference (with dis- cussion). Statistical Science 3, 213-238.
Rice, J. (1984) Bandwidth choice for nonparametric regression. Ann. Statist. 12, 1215-1230.
Robinson, G.K. (1982) Behrens-Fisher Problem. In Encyclopedia of Sta- tistical Science, Vol. 1. Ed. S. Kotz and N.L. Johnson. Wiley, New York. pp. 205-209.
Robinson, J.A. (1986) Bootstrap and randomization confidence inter- vals. Proceedings of the Pacific Statistical Congress, 20-24 May 1985, Auckland (I.S. Francis, B.F.J. Manly, and F.C. Lam, eds.), North- Holland, Groningen, pages 49-50.
Robinson, J.A. (1987) Nonparametric confidence intervals in regression: The bootstrap and randomization methods. New Perspectives in The- oretical and Applied Statistics (M.L. Puri, J.P. Vilaplana, and W. Wertz, eds.), Wiley, New York, pages 243-256.
Romano, J.P . (1988) A bootstrap revival of some nonparametric distance tests. J. Amer. Statist. Assoc. 83, 698-708.
Romano, J.P. (1989) Bootstrap and randomization tests of some non- parametric hypotheses. Ann. Statist. 17, 141-159.
Rousseeuw, P. (1984) Least median of squares regression. J. Amer. Statist. Assoc. 79, 871-80.
Royall, R. M. (1986) Model robust confidence intervals using maximum likelihood estimators. Int. Statist. Rev. 54, 221-6.
Rubin, D.B. (1981) The Bayesian bootstrap. Ann. Statist. 9, 130-134. Schenker, N. (1985) Qualms about bootstrap confidence intervals. J.
Amer. Statist. Assoc. 80, 360-361.
Scholz, F.W. (1980) Towards a unified definition of maximum likelihood.
Can. J. Statist. 8, 193- 203.
Schwarz, G. (1978) Estimating the dimension of a model. Ann. Statist.
6, 461-464.
Scott. D. (1992) Multivariate density estimation: theory, practice and
visualization. Wiley, New York.
Sen, P.K. (1988) Functional jackknifing: rationality and general asymp-
totics. Ann. Statist. 16, 450-469.
Shao, J. (1988) On resampling methods for bias and variance in linear models. Ann. Statist. 16, 986-988.
Shao, J. (1991) Consistency of jackknife variance estimators. Statistics 22, 49-57.
Shao, J. (1993) Linear model selection via cross-validation. To appear
J. Amer. Statist. Assoc..
Shao, J. and Wu, C.F.J. (1989) A general theory for jackknife variance
estimation. Ann. Statist. 17, 1176-1197.
Sheather, S.J. (1987) Assessing the accuracy of the sample median: es-
timated standard errors versus interpolated confidence intervals. In Statistical Data Analysis Based on the L1-Norm. Ed. Y. Dodge, pages 203-215. North Holland, Amsterdam.
Shorack, G.P. (1982) Bootstrapping robust regression. Comm. Statist. A. 11, 961-972.
Silverman, B.W. (1981) Using kernel density estimates to investigate multimodality. J. Royal. Statist. Soc. B 43, 97-99.
Silverman, B.W. (1983) Some properties of a test for multimodality based on kernel density estimates. In Probability, Statistics, and Anal- ysis Eds. J.F.C. Kingman, and G.E.H. Reuter, 248-260. Cambridge Univ. Press, Cambridge, U.K.
Silverman, B.W. (1985) Some aspects of the spline smoothing approach to non-parametric regression curve fitting. J. Royal. Statist. Soc. B 36, 1-52.
Silverman, B.W. (1986) Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.
Silverman, B.W. and Young, G.A. (1987) The bootstrap: to smooth or not to smooth? Biometrika 74, 469-479.
Silvey, S.D. (1975) Statistical inference. Chapman and Hall, London. Simon, J.L. and Bruce, P (1991) Resampling: a tool for everyday statis-
tical work. Chance 4, 22-32.
Singh, K. (1981) On the asymptotic accuracy of Efron's bootstrap. Ann.
Statist. 9, 1187-1195.
Sitter R. V. (1992) Bootstrap methods for survey data. Can. J. Statist.,
20, 135-154.
Sitter, R. V. (1993) Balanced repeated replications based on orthogonal
multi-arrays. To appear, Biometrika, 80
Snedecor, G.W. and Cochran, W.G. (1980) Statistical methods. Seventh
edition. Iowa State University.
Stein, C. (1956) Efficient nonparametric testing and estimation, Pro-
ceedings of the Third Berkeley Symposium, University of California
Press, 187-196, Berkeley.
Stein, M. (1987) Large sample properties of simulations using Latin
hypercube sampling. Technometrics 29, 143-151.
Stine, R.A. (1985) Bootstrap prediction intervals for regression. J. Amer.
Statist. Assoc. 80, 1026-1031.
Stone, M. (1974) Cross-validation choice and assessment of statistical predictions. J. Royal. Statist. Soc. B 36, 111-147.
Stone, M. (1977) An asymptotic equivalence of choice of model by cross- validation and Akaike's criterion. J. Royal. Statist. Soc. B 39, 44-7.
Swanepoel, J.W.H., Van Wyk, J.W.J., and Venter, J.H. (1983) Fixed width confidence intervals based on bootstrap procedures. Sequential Anal. 2, 289-310.
Taylor, C.C. (1989) Bootstrap choice of the smoothing parameter in kernel density estimation. Biometrika 76, 705-712.
Therneau, T. M. (1983) Variance reduction techniques for the bootstrap. Ph.D. thesis, Department of Statistics, Stanford University.
Thisted, R.A. (1986) Elements of statistical computing. Chapman and Hall, London.
Tibshirani, R. (1985) Bootstrapping computations. Proc. of the SAS Users group conference, Reno, Nevada.
Tibshirani, R. (1986) Bootstrap confidence intervals. Computer Science and Statistics: Proceedings of the 18th Symposium on the Interface (J.T. Boardman, ed.), Amer. Stat. Assoc. Washington, DC, pages 267-273.
Tibshirani, R. (1988) Variance stabilization and the bootstrap. Biometrika 75, 433-444.
Tibshirani, R. (1992) Comment on "Two guidelines for bootstrap hy- pothesis testing", by Hall, P. and Wilson, S.R. Biometrics 48, 969- 970.
Tukey, J.W. (1958) Bias and confidence in not quite large samples. (Ab- stract.) Ann. Math. Statist. 29, 614.
Wahba, G. (1980) Spline bases, regularization, and generalized cross validation for solving approximation problems with large quantities of noisy data. In Cheney, W ., editor, Approximation Theory III, pages 905-912. Academic Press.
Wahba, G. (1990) Spline Functions for Observational Data. CBMS-NSF Regional Conference series, SIAM. Philadelphia.
Wasserman, L. (1990) Belief functions and statistical inference. Can. J. Statist. 18, 193-196.
Wang, S.J. (1992) General saddlepoint approximations in the bootstrap. Statist. Prob. Letters 13, 61-66.
Weber, N.C. (1984) On resampling techniques for regression models.
Proceedings of the Pacific Statistical Congress, 20-24 May 1985, Auck- land (I.S. Francis, B.F.J. Manly, and F.c. Lam, eds.), North-Holland, Groningen, pp. 51-55.
Weisberg, S. (1980) Applied linear regression. Wiley, New York.
Welch, B.L. (1947) The generalization of "Student's" problem when sev- eral different population variances are involved. Biometrika 34, 28-35. White, H. (1981) Consequences and detection of misspecified nonlinear
regression Models. J. Amer. Statist. Assoc. 76, 419-33.
参考資料(References)
Data Scientist の基礎(2)
https://qiita.com/kaizen_nagoya/items/8b2f27353a9980bf445c
岩波数学辞典 二つの版がCDに入ってお得
https://qiita.com/kaizen_nagoya/items/1210940fe2121423d777
岩波数学辞典
https://qiita.com/kaizen_nagoya/items/b37bfd303658cb5ee11e
アンの部屋（人名から学ぶ数学：岩波数学辞典）英語(24)
https://qiita.com/kaizen_nagoya/items/e02cbe23b96d5fb96aa1
<この項は書きかけです。順次追記します。>
This article is not completed. I will add some words and/or centences in order.
Este artículo no está completo. Agregaré algunas palabras en orden.
知人資料
' @kazuo_reve 私が効果を確認した「小川メソッド」
https://qiita.com/kazuo_reve/items/a3ea1d9171deeccc04da
' @kazuo_reve 新人の方によく展開している有益な情報
https://qiita.com/kazuo_reve/items/d1a3f0ee48e24bba38f1
' @kazuo_reve Vモデルについて勘違いしていたと思ったこと
https://qiita.com/kazuo_reve/items/46fddb094563bd9b2e1e
自己記事一覧
Qiitaで逆リンクを表示しなくなったような気がする。時々、スマフォで表示するとあらわることがあり、完全に削除したのではなさそう。2024年４月以降、せっせとリンクリストを作り、統計を取って確率を説明しようとしている。2025年２月末を目標にしていた。
一覧の一覧( The directory of directories of mine.) Qiita(100)
https://qiita.com/kaizen_nagoya/items/7eb0e006543886138f39
仮説（0）一覧（目標100現在40）
https://qiita.com/kaizen_nagoya/items/f000506fe1837b3590df
Qiita(0)Qiita関連記事一覧（自分）
https://qiita.com/kaizen_nagoya/items/58db5fbf036b28e9dfa6
Error一覧 error(0)
https://qiita.com/kaizen_nagoya/items/48b6cbc8d68eae2c42b8
C++ Support(0)
https://qiita.com/kaizen_nagoya/items/8720d26f762369a80514
Coding(0) Rules, C, Secure, MISRA and so on
https://qiita.com/kaizen_nagoya/items/400725644a8a0e90fbb0
Ethernet 記事一覧 Ethernet(0)
https://qiita.com/kaizen_nagoya/items/88d35e99f74aefc98794
Wireshark 一覧 wireshark(0)、Ethernet(48)
https://qiita.com/kaizen_nagoya/items/fbed841f61875c4731d0
線網（Wi-Fi）空中線(antenna)(0) 記事一覧(118/300目標)
https://qiita.com/kaizen_nagoya/items/5e5464ac2b24bd4cd001
なぜdockerで機械学習するか 書籍・ソース一覧作成中 (目標100)
https://qiita.com/kaizen_nagoya/items/ddd12477544bf5ba85e2
プログラムちょい替え（0）一覧:4件
https://qiita.com/kaizen_nagoya/items/296d87ef4bfd516bc394
言語処理100本ノックをdockerで。python覚えるのに最適。:10+12
https://qiita.com/kaizen_nagoya/items/7e7eb7c543e0c18438c4
Python(0)記事をまとめたい。
https://qiita.com/kaizen_nagoya/items/088c57d70ab6904ebb53
安全（0）安全工学シンポジウムに向けて: 21
https://qiita.com/kaizen_nagoya/items/c5d78f3def8195cb2409
プログラマによる、プログラマのための、統計(0)と確率のプログラミングとその後
https://qiita.com/kaizen_nagoya/items/6e9897eb641268766909
転職(0)一覧
https://qiita.com/kaizen_nagoya/items/f77520d378d33451d6fe
技術士(0)一覧
https://qiita.com/kaizen_nagoya/items/ce4ccf4eb9c5600b89ea
Reserchmap(0) 一覧
https://qiita.com/kaizen_nagoya/items/506c79e562f406c4257e
物理記事 上位100
https://qiita.com/kaizen_nagoya/items/66e90fe31fbe3facc6ff
量子(0) 計算機, 量子力学
https://qiita.com/kaizen_nagoya/items/1cd954cb0eed92879fd4
数学関連記事１００
https://qiita.com/kaizen_nagoya/items/d8dadb49a6397e854c6d
coq(0) 一覧
https://qiita.com/kaizen_nagoya/items/d22f9995cf2173bc3b13
統計(0)一覧
https://qiita.com/kaizen_nagoya/items/80d3b221807e53e88aba
図(0) state, sequence and timing. UML and お絵描き
https://qiita.com/kaizen_nagoya/items/60440a882146aeee9e8f
色(0) 記事100書く切り口
https://qiita.com/kaizen_nagoya/items/22331c0335ed34326b9b
品質一覧
https://qiita.com/kaizen_nagoya/items/2b99b8e9db6d94b2e971
言語・文学記事 １００
https://qiita.com/kaizen_nagoya/items/42d58d5ef7fb53c407d6
医工連携関連記事一覧
https://qiita.com/kaizen_nagoya/items/6ab51c12ba51bc260a82
水の資料集(0) 方針と成果
https://qiita.com/kaizen_nagoya/items/f5dbb30087ea732b52aa
自動車 記事 １００
https://qiita.com/kaizen_nagoya/items/f7f0b9ab36569ad409c5
通信記事１００
https://qiita.com/kaizen_nagoya/items/1d67de5e1cd207b05ef7
日本語（０）一欄
https://qiita.com/kaizen_nagoya/items/7498dcfa3a9ba7fd1e68
英語(0) 一覧
https://qiita.com/kaizen_nagoya/items/680e3f5cbf9430486c7d
音楽 一覧(0)
https://qiita.com/kaizen_nagoya/items/b6e5f42bbfe3bbe40f5d
「@kazuo_reve 新人の方によく展開している有益な情報」確認一覧
https://qiita.com/kaizen_nagoya/items/b9380888d1e5a042646b
鉄道（０）鉄道のシステム考察はてっちゃんがてつだってくれる
https://qiita.com/kaizen_nagoya/items/faa4ea03d91d901a618a
OSEK OS設計の基礎 OSEK(100)
https://qiita.com/kaizen_nagoya/items/7528a22a14242d2d58a3
coding (101) 一覧を作成し始めた。omake:最近のQiitaで表示しない5つの事象
https://qiita.com/kaizen_nagoya/items/20667f09f19598aedb68
官公庁・学校・公的団体（NPOを含む）システムの課題、官（０）
https://qiita.com/kaizen_nagoya/items/04ee6eaf7ec13d3af4c3
「はじめての」シリーズ ベクタージャパン
https://qiita.com/kaizen_nagoya/items/2e41634f6e21a3cf74eb
AUTOSAR(0)Qiita記事一覧, OSEK(75)
https://qiita.com/kaizen_nagoya/items/89c07961b59a8754c869
プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945
LaTeX(0) 一覧
https://qiita.com/kaizen_nagoya/items/e3f7dafacab58c499792
自動制御、制御工学一覧（０）
https://qiita.com/kaizen_nagoya/items/7767a4e19a6ae1479e6b
Rust(0) 一覧
https://qiita.com/kaizen_nagoya/items/5e8bb080ba6ca0281927
programの本質は計画だ。programは設計だ。
https://qiita.com/kaizen_nagoya/items/c8545a769c246a458c27
登壇直後版 色使い(JIS安全色) Qiita Engineer Festa 2023〜私しか得しないニッチな技術でLT〜 スライド編 0.15
https://qiita.com/kaizen_nagoya/items/f0d3070d839f4f735b2b
プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945
逆も真：社会人が最初に確かめるとよいこと。OSEK(69)、Ethernet(59)
https://qiita.com/kaizen_nagoya/items/39afe4a728a31b903ddc
統計の嘘。仮説（127）
https://qiita.com/kaizen_nagoya/items/63b48ecf258a3471c51b
自分の言葉だけで論理展開できるのが天才なら、文章の引用だけで論理展開できるのが秀才だ。仮説（136）
https://qiita.com/kaizen_nagoya/items/97cf07b9e24f860624dd
参考文献駆動執筆(references driven writing)・デンソークリエイト編
https://qiita.com/kaizen_nagoya/items/b27b3f58b8bf265a5cd1
「何を」よりも「誰を」。１０年後のために今見習いたい人たち
https://qiita.com/kaizen_nagoya/items/8045978b16eb49d572b2
Qiitaの記事に３段階または５段階で到達するための方法
https://qiita.com/kaizen_nagoya/items/6e9298296852325adc5e
出力(output)と呼ばないで。これは状態(state)です。
https://qiita.com/kaizen_nagoya/items/80b8b5913b2748867840
祝休日・謹賀新年 2025年の目標
https://qiita.com/kaizen_nagoya/items/dfa34827932f99c59bbc
Qiita 1年間をまとめた「振り返りページ」＠2024
https://qiita.com/kaizen_nagoya/items/ed6be239119c99b15828
2024 参加・主催Calendarと投稿記事一覧 Qiita(248)
https://qiita.com/kaizen_nagoya/items/d80b8fbac2496df7827f
主催Calendar2024分析 Qiita(254)
https://qiita.com/kaizen_nagoya/items/15807336d583076f70bc
Calendar 統計
https://qiita.com/kaizen_nagoya/items/e315558dcea8ee3fe43e
LLM 関連 Calendar 2024
https://qiita.com/kaizen_nagoya/items/c36033cf66862d5496fa
Large Language Model Related Calendar
https://qiita.com/kaizen_nagoya/items/3beb0bc3fb71e3ae6d66
博士論文 Calendar 2024 を開催します。
https://qiita.com/kaizen_nagoya/items/51601357efbcaf1057d0
博士論文(0)関連記事一覧
https://qiita.com/kaizen_nagoya/items/8f223a760e607b705e78
coding (101) 一覧を作成し始めた。omake:最近のQiitaで表示しない5つの事象
https://qiita.com/kaizen_nagoya/items/20667f09f19598aedb68
あなたは「勘違いまとめ」から、勘違いだと言っていることが勘違いだといくつ見つけられますか。人間の間違い(human error(125))の種類と対策
https://qiita.com/kaizen_nagoya/items/ae391b77fffb098b8fb4
プログラマの「プログラムが書ける」思い込みは強みだ。３つの理由。仮説（168）統計と確率(17) , OSEK(79)
https://qiita.com/kaizen_nagoya/items/bc5dd86e414de402ec29
出力(output)と呼ばないで。これは状態(state)です。
https://qiita.com/kaizen_nagoya/items/80b8b5913b2748867840
これからの情報伝達手段の在り方について考えてみよう。炎上と便乗。
https://qiita.com/kaizen_nagoya/items/71a09077ac195214f0db
ISO/IEC JTC1 SC7 Software and System Engineering
https://qiita.com/kaizen_nagoya/items/48b43f0f6976a078d907
アクセシビリティの知見を発信しよう！（再び）
https://qiita.com/kaizen_nagoya/items/03457eb9ee74105ee618
統計論及確率論輪講（再び）
https://qiita.com/kaizen_nagoya/items/590874ccfca988e85ea3
読者の心をグッと惹き寄せる7つの魔法
https://qiita.com/kaizen_nagoya/items/b1b5e89bd5c0a211d862
「@kazuo_reve 新人の方によく展開している有益な情報」確認一覧
https://qiita.com/kaizen_nagoya/items/b9380888d1e5a042646b
ソースコードで議論しよう。日本語で議論するの止めましょう（あるプログラミング技術の議論報告）
https://qiita.com/kaizen_nagoya/items/8b9811c80f3338c6c0b0
脳内コンパイラの3つの危険
https://qiita.com/kaizen_nagoya/items/7025cf2d7bd9f276e382
心理学の本を読むよりはコンパイラ書いた方がよくね。仮説（34）
https://qiita.com/kaizen_nagoya/items/fa715732cc148e48880e
NASAを超えるつもりがあれば読んでください。
https://qiita.com/kaizen_nagoya/items/e81669f9cb53109157f6
データサイエンティストの気づき!「勉強して仕事に役立てない人。大嫌い!!」『それ自分かも?』ってなった!!!
https://qiita.com/kaizen_nagoya/items/d85830d58d8dd7f71d07
「ぼくの好きな先生」「人がやらないことをやれ」プログラマになるまで。仮説（37）
https://qiita.com/kaizen_nagoya/items/53e4bded9fe5f724b3c4
なぜ経済学徒を辞め、計算機屋になったか（経済学部入学前・入学後・卒業後対応） 転職(1)
https://qiita.com/kaizen_nagoya/items/06335a1d24c099733f64
プログラミング言語教育のXYZ。 仮説（52）
https://qiita.com/kaizen_nagoya/items/1950c5810fb5c0b07be4
【24卒向け】9ヶ月後に年収１０００万円を目指す。二つの関門と三つの道。
https://qiita.com/kaizen_nagoya/items/fb5bff147193f726ad25
「【25卒向け】Qiita Career Meetup for STUDENT」予習の勧め
https://qiita.com/kaizen_nagoya/items/00eadb8a6e738cb6336f
大学入試不合格でも筆記試験のない大学に入って卒業できる。卒業しなくても博士になれる。
https://qiita.com/kaizen_nagoya/items/74adec99f396d64b5fd5
全世界の不登校の子供たち「博士論文」を書こう。世界子供博士論文遠隔実践中心 安全(99)
https://qiita.com/kaizen_nagoya/items/912d69032c012bcc84f2
日本のプログラマが世界で戦える16分野。仮説(53),統計と確率(25) 転職(32)、Ethernet(58)
https://qiita.com/kaizen_nagoya/items/a7e634a996cdd02bc53b
小川メソッド 覚え（書きかけ）
https://qiita.com/kaizen_nagoya/items/3593d72eca551742df68
DoCAP（ドゥーキャップ）って何ですか？
https://qiita.com/kaizen_nagoya/items/47e0e6509ab792c43327
views 20,000越え自己記事一覧
https://qiita.com/kaizen_nagoya/items/58e8bd6450957cdecd81
Views１万越え、もうすぐ１万記事一覧 最近いいねをいただいた213記事
https://qiita.com/kaizen_nagoya/items/d2b805717a92459ce853
amazon 殿堂入りNo1レビュアになるまで。仮説(102)
https://qiita.com/kaizen_nagoya/items/83259d18921ce75a91f4
100以上いいねをいただいた記事16選
https://qiita.com/kaizen_nagoya/items/f8d958d9084ffbd15d2a
水道局10年(1976,4-1986,3)を振り返る
https://qiita.com/kaizen_nagoya/items/707fcf6fae230dd349bf
小川清最終講義、最終講義（再）計画, Ethernet(100) 英語(100) 安全(100)
https://qiita.com/kaizen_nagoya/items/e2df642e3951e35e6a53
＜この記事は個人の過去の経験に基づく個人の感想です。現在所属する組織、業務とは関係がありません。＞
This article is an individual impression based on my individual experience. It has nothing to do with the organization or business to which I currently belong.
Este artículo es una impresión personal basada en mi experiencia personal. No tiene nada que ver con la organización o empresa a la que pertenezco actualmente.
文書履歴(document history)
ver. 0.01 初稿 20250518
最後までおよみいただきありがとうございました。
いいね 💚、フォローをお願いします。
Thank you very much for reading to the last sentence.
Please press the like icon 💚 and follow me for your happy life.
Muchas gracias por leer hasta la última oración.
Por favor, haz clic en el ícono Me gusta 💚 y sígueme para tener una vida feliz.