In this article, we propose generalized two-parameter (GTP) estimators and an algorithm for the estimation of shrinkage parameters to combat multicollinearity in multinomial logit regression model. addition, mean squared error properties are derived. A simulation study is conducted investigate performance proposed different sample sizes, degrees multicollinearity, number explanatory variables. ...

PURPOSE To evaluate the factors affecting postoperative visual function in eyes with Artisan phakic intraocular lenses (PIOLs). METHODS This study examined a retrospective noncomparative case series. Phakic IOLs were implanted in 60 eyes of 36 patients with high myopia. Contrast sensitivity for spatial frequencies of 1.1, 1.7, 2.6, 4.2, 6.6, and 10:4 cycles per degree with and without glare a...

Regression models with good fitting but no predictive ability are sometimes chance correlations and often show some pathological features such as multicollinearity, overfitting, and inclusion of noisy/spurious variables. This problem is well known and of the utmost importance. The present paper proposes some criteria that are to be fulfilled as conditions for model acceptability, the aim being ...

Present methodological research on geographically weighted regression (GWR) focuses primarily on extensions of the basic GWR model, while ignoring well-established diagnostics tests commonly used in standard global regression analysis. This paper investigates multicollinearity issues surrounding the local GWR coefficients at a single location and the overall correlation between GWR coefficients...

OBJECTIVE Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. METHODS Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregre...

Markov chain Monte Carlo (MCMC) algorithms have greatly facilitated the popularity of Bayesian variable selection and model averaging in problems with high-dimensional covariates where enumeration of the model space is infeasible. A variety of such algorithms have been proposed in the literature for sampling models from the posterior distribution in Bayesian variable selection. Ghosh and Clyde ...

Under the background of big data era today, once been widely used method – multiple linear regressions can not satisfy people’s need to handle big data any more because of its bad characteristics such as multicollinearity, instability, subjectivity in model chosen etc. Contrary to MLR, LASSO method has many good natures. it is stable and can handle multicollinearity and successfully select the ...

Fuzzy clusterwise regression has been a useful method for investigating cluster-level heterogeneity of observations based on linear regression. This method integrates fuzzy clustering and ordinary least-squares regression, thereby enabling to estimate regression coefficients for each cluster and fuzzy cluster memberships of observations simultaneously. In practice, however, fuzzy clusterwise re...