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A comparison of model choice strategies for logistic regression


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Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov, F. Csaki (Eds.), Proceedings of the Second International Symposium on Information Theory (pp. 267-281). Budapest: Akademiai Kiado. Akaike H. ( 1973 ). Information theory and an extension of the maximum likelihood principle . In Petrov B. N. Csaki F. (Eds.), Proceedings of the Second International Symposium on Information Theory (pp. 267 - 281 ). Budapest : Akademiai Kiado . Search in Google Scholar

Ayers, K. L., Cordell, H. J. (2010). SNP selection in genome-wide and candidate gene studies via penalized logistic regression. Genetic Epidemiology, 34(8), 879-891. Ayers K. L. Cordell H. J. ( 2010 ). SNP selection in genome-wide and candidate gene studies via penalized logistic regression . Genetic Epidemiology , 34 ( 8 ), 879 - 891 . Search in Google Scholar

Bejaei, M., Wiseman, K., Cheng, K. M. (2015). Developing logistic regression models using purchase attributes and demographics to predict the probability of purchases of regular and specialty eggs. British Poultry Science, 56(4), 425-435. Bejaei M. Wiseman K. Cheng K. M. ( 2015 ). Developing logistic regression models using purchase attributes and demographics to predict the probability of purchases of regular and specialty eggs . British Poultry Science , 56 ( 4 ), 425 - 435 . Search in Google Scholar

Buse, A. (1982). The likelihood ratio, Wald, and Lagrange multiplier tests: An expository note. The American Statistician, 36(3a), 153-157. Buse A. ( 1982 ). The likelihood ratio, Wald, and Lagrange multiplier tests: An expository note . The American Statistician , 36 ( 3a ), 153 - 157 . Search in Google Scholar

Cavanaugh, J. E. (1997). Unifying the derivations for the Akaike and corrected Akaike information criteria. Statistics Probability Letters, 33(2), 201-208. Cavanaugh J. E. ( 1997 ). Unifying the derivations for the Akaike and corrected Akaike information criteria . Statistics Probability Letters , 33 ( 2 ), 201 - 208 . Search in Google Scholar

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. (2nd ed). Routledge. Cohen J. ( 1988 ). Statistical Power Analysis for the Behavioral Sciences . ( 2nd ed). Routledge . Search in Google Scholar

Friedman, J., Hastie, T., Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1. Friedman J. Hastie T. Tibshirani R. ( 2010 ). Regularization paths for generalized linear models via coordinate descent . Journal of Statistical Software , 33 ( 1 ), 1 . Search in Google Scholar

Hoerl, A. E., Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67. Hoerl A. E. Kennard R. W. ( 1970 ). Ridge regression: Biased estimation for nonorthogonal problems . Technometrics , 12 ( 1 ), 55 - 67 . Search in Google Scholar

Hurvich, C. M., Tsai, C-L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297-307. Hurvich C. M. Tsai C-L. ( 1989 ). Regression and time series model selection in small samples . Biometrika , 76 ( 2 ), 297 - 307 . Search in Google Scholar

Imori, S., Yanagihara, H., Wakaki, H. (2014). Simple formula for calculating bias-corrected AIC in generalized linear models. Scandinavian Journal of Statistics, 41(2), 535-555. Imori S. Yanagihara H. Wakaki H. ( 2014 ). Simple formula for calculating bias-corrected AIC in generalized linear models . Scandinavian Journal of Statistics , 41 ( 2 ), 535 - 555 . Search in Google Scholar

Karhunen, M. (2019). Algorithmic sign prediction and covariate selection across eleven international stock markets. Expert Systems with Applications, 115, 256-263. Karhunen M. ( 2019 ). Algorithmic sign prediction and covariate selection across eleven international stock markets . Expert Systems with Applications , 115 , 256 - 263 . Search in Google Scholar

McGullagh, P., Nelder J. A. (1989). GeneralizedLinearModels. (2nd ed). Chapman Hall/CRC. McGullagh P. Nelder J. A. ( 1989 ). Generalized Linear Models . ( 2nd ed). Chapman Hall/CRC . Search in Google Scholar

McQuarrie, A. D. (1999). A small-sample correction for the Schwarz SIC model selection criterion. Statistics Probability Letters, 44(1) 79-86. McQuarrie A. D. ( 1999 ). A small-sample correction for the Schwarz SIC model selection criterion . Statistics Probability Letters , 44 ( 1 ) 79 - 86 . Search in Google Scholar

Qian, G. Q., Field, C. (2002). Using MCMC for logistic regression model selection involving large number of candidate models. In Fang, KT., Niederreiter, H., Hickernell, F.J. (Eds.), Monte Carlo and Quasi-Monte Carlo Methods 2000. Springer, Berlin, Heidelberg. Qian G. Q. Field C. ( 2002 ). Using MCMC for logistic regression model selection involving large number of candidate models . In Fang KT. Niederreiter H. Hickernell F.J. (Eds.), Monte Carlo and Quasi-Monte Carlo Methods 2000 . Springer , Berlin, Heidelberg . Search in Google Scholar

Qian, G. Q., Künsch, H. R. (1998). Some notes on Rissanen’s stochastic complexity. IEEE Transactions on Information Theory, 44(2), 782-786. Qian G. Q. Künsch H. R. ( 1998 ). Some notes on Rissanen’s stochastic complexity . IEEE Transactions on Information Theory , 44 ( 2 ), 782 - 786 . Search in Google Scholar

Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14(5), 465-471. Rissanen J. ( 1978 ). Modeling by shortest data description . Automatica , 14 ( 5 ), 465 - 471 . Search in Google Scholar

Saha, T. K., Pal, S. (2019). Exploring physical wetland vulnerability of Atreyee river basin in India and Bangladesh using logistic regression and fuzzy logic approaches. Ecological Indicators, 98, 251-265. Saha T. K. Pal S. ( 2019 ). Exploring physical wetland vulnerability of Atreyee river basin in India and Bangladesh using logistic regression and fuzzy logic approaches . Ecological Indicators , 98 , 251 - 265 . Search in Google Scholar

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 461-464. Schwarz G. ( 1978 ). Estimating the dimension of a model . The Annals of Statistics , 461 - 464 . Search in Google Scholar

Sugiura, N. (1978). Further analysis of the data by Akaike’s information criterion and the finite corrections. Communications in Statistics-Theory and Methods, 7(1), 13-26. Sugiura N. ( 1978 ). Further analysis of the data by Akaike’s information criterion and the finite corrections . Communications in Statistics-Theory and Methods , 7 ( 1 ), 13 - 26 . Search in Google Scholar

Tay, J. K., Narasimhan, B., Hastie, T. (2023). Elastic net regularization paths for all generalized linear models. Journal of Statistical Software, 106. Tay J. K. Narasimhan B. Hastie T. ( 2023 ). Elastic net regularization paths for all generalized linear models . Journal of Statistical Software , 106 . Search in Google Scholar

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267-288. Tibshirani R. ( 1996 ). Regression shrinkage and selection via the lasso . Journal of the Royal Statistical Society Series B: Statistical Methodology , 58 ( 1 ), 267 - 288 . Search in Google Scholar

Zhang, Y Y, Zhou, X. B., Wang, Q. Z., Zhu, X. Y (2017). Quality of reporting of multivariable logistic regression models in Chinese clinical medical journals. Medicine, 96(21). Zhang Y Y Zhou X. B. Wang Q. Z. Zhu X. Y ( 2017 ). Quality of reporting of multivariable logistic regression models in Chinese clinical medical journals . Medicine , 96 ( 21 ). Search in Google Scholar

Zhou, X. B., Wang, X. D., Dougherty, E. R. (2005). Gene selection using logistic regressions based on AIC, BIC and MDL criteria. New Mathematics and Natural Computation, 1(01), 129-145. Zhou X. B. Wang X. D. Dougherty E. R. ( 2005 ). Gene selection using logistic regressions based on AIC, BIC and MDL criteria . New Mathematics and Natural Computation , 1 ( 01 ), 129 - 145 . Search in Google Scholar

Zou, H. (2006). The adaptive Lasso and its oracle properties. Journal of the American Statistical Association, 101(476), 1418-1429. Zou H. ( 2006 ). The adaptive Lasso and its oracle properties . Journal of the American Statistical Association , 101 ( 476 ), 1418 - 1429 . Search in Google Scholar

eISSN:
2543-683X
Język:
Angielski
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Computer Sciences, Information Technology, Project Management, Databases and Data Mining