References
- 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.
- 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.
- 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.
- Buse, A. (1982). The likelihood ratio, Wald, and Lagrange multiplier tests: An expository note. The American Statistician, 36(3a), 153-157.
- Cavanaugh, J. E. (1997). Unifying the derivations for the Akaike and corrected Akaike information criteria. Statistics Probability Letters, 33(2), 201-208.
- Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. (2nd ed). Routledge.
- Friedman, J., Hastie, T., Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1.
- Hoerl, A. E., Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.
- Hurvich, C. M., Tsai, C-L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297-307.
- 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.
- Karhunen, M. (2019). Algorithmic sign prediction and covariate selection across eleven international stock markets. Expert Systems with Applications, 115, 256-263.
- McGullagh, P., Nelder J. A. (1989). GeneralizedLinearModels. (2nd ed). Chapman Hall/CRC.
- McQuarrie, A. D. (1999). A small-sample correction for the Schwarz SIC model selection criterion. Statistics Probability Letters, 44(1) 79-86.
- 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., Künsch, H. R. (1998). Some notes on Rissanen’s stochastic complexity. IEEE Transactions on Information Theory, 44(2), 782-786.
- Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14(5), 465-471.
- 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.
- Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 461-464.
- 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.
- Tay, J. K., Narasimhan, B., Hastie, T. (2023). Elastic net regularization paths for all generalized linear models. Journal of Statistical Software, 106.
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267-288.
- 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).
- 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.
- Zou, H. (2006). The adaptive Lasso and its oracle properties. Journal of the American Statistical Association, 101(476), 1418-1429.