The binary logistic regression model (LRM) is practical in situations when the response variable (RV) dichotomous. maximum likelihood estimator (MLE) generally considered to estimate LRM parameters. However, presence of multicollinearity (MC), MLE not correct choice due its inflated standard deviation (SD) and errors (SE) estimates. To combat MC, commonly used biased estimators, i.e., Ridge est...

In this paper, we study the identification of Finite Impulse Response systems in a particular context: data on input and output are obtained with one-bit quantizers, thresholds quantizers can be different from zero. A three-step algorithm is proposed these binary-valued measurements. This based normal distribution noises. The appropriately analyzed: it shown to asymptotically unbiased, its asym...

This paper studies single equation models for binary outcomes incorporating instrumental variable restrictions. The models are incomplete in the sense that they place no restriction on the way in which values of endogenous variables are generated. The models are set, not point, identifying. The paper explores the nature of set identi…cation in single equation IV models in which the binary outco...

Statistical inference is based generally on some estimates that are functions of the data. Bootstrapping procedure offers strategies to estimate or approximate the sampling distribution of a statistic. Logistics regression model with binary response is commonly used. This paper focuses on the behavior of bootstrapping pseudo R 2 measures in logistic regression model. Simulation and real data re...

The idea of dimension-reduction without loss of information can be quite helpful for guiding the construction of summary plots in regression without requiring a pre-specified model. Focusing on the central subspace, we investigate such “sufficient” dimension-reduction in regressions with a binary response. Three existing methods, SIR and pHd and SAVE, and one new method DOC are studied for thei...