References
[1] J. A. Nelder & R.W.M. Wedderburn. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A, 135 (3), 370-384.
[2] Kung-Yee Liang & Scott L. Zeger. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13-22.
[3] McDonald & Barry W. (1993). Estimating logistics regression parameters for bivariate binary data. Journal of the Royal Statistical Society, Series B, 55 (2), 391-397.
[4] Garrett M. Fitzmaurice. (1995). A caveat concerning independence estimating equations with multivariate binary data. Biometrics, 51 (1), 309-317.
[5] Justine Shults & N. Rao Chaganty. (1998). Analysis of serially correlated data using quasi-least squares. Biometrics, 54 (4), 1622-1630.
[6] Vicente Nunez-Anton & George G. Woodworth. (1994). Analysis of longitudinal data with unequally spaced observations and time-dependent correlated errors. Biometrics, 50 (2), 445-456.
[7] N. Rao Chaganty. (1997). An alternative approach to the analysis of longitudinal data via generalized estimating equations. Journal of Statistical Planning and Inference, 63 (1), 39-54.
[8] James E. Gentle. (2007). Matrix algebra theory, computations, and applications in statistics. Springer Texts in Statistics, Springer, New York.
[9] Zhang Jin Hua & Xue Liu Gen. (2017). Quadratic inference functions for generalized partially linear models with longitudinal data. Chinese Journal of Applied Probability and Statistics, 33 (4), 417-432.
[10] Peter J. Blockwell & Richard A. Davis. (1991). Time series: Theory and methods second edition. Springer Series in Statistics, Springer, New York.
[11] Ross L. Prentice. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics, 44 (4), 1033-1048.
[12] Kung-Yee Liang, Scott L. Zeger, & Bahjat Qaqish. (1992). Multivariate regression analyses for categorical data. Journal of the Royal Statistical Society, Series B, 54 (1), 3-40.
[13] A. F. Desmond. (1997). Optimal estimating functions, quasi-likelihood and statistical modelling. Journal of Statistical Planning and Inference, 60 (1), 77-104.
[14] Dongyang Dou, et al. (2019). Classification of coal and gangue under multiple surface conditions via machine vision and relief-SVM, Powder Technology.
[15] Lv Pengfei & Qiu Lin. (2021). Research on classification prediction of mine rock burst based on PSO-LSSVM. Mining Safety and Environmental Protection, (01).
[16] Ma Shaojuan, et al. (2019). Prediction of chaotic time series based on unbiased least squares support vector machine. Mathematical Practice and Understanding, 53(06): 150-159.
[17] Zhang Yi. (2023). Research and application of time series analysis based on ARMIA-LSTM mixed model. Yangtze University.
[18] Wang Luyao. (2022). A method for solving separable nonlinear least squares problem based on matrix decomposition and its application. Journal of Surveying and Mapping, 51(11): 2317-2327.