Liu-Type Logistic Estimators with Optimal Shrinkage Parameter
نویسندگان
چکیده
منابع مشابه
Liu-Type Logistic Estimators with Optimal Shrinkage Parameter
Multicollinearity in logistic regression affects the variance of the maximum likelihood estimator negatively. In this study, Liu-type estimators are used to reduce the variance and overcome the multicollinearity by applying some existing ridge regression estimators to the case of logistic regression model. A Monte Carlo simulation is given to evaluate the performances of these estimators when t...
متن کاملKernel Mean Shrinkage Estimators
A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principal component analysis, and it also forms the core inference step of modern kernel methods that rely on embedding probability distributions in RKHSs. Given a finite sample, an empirical average has been used commonly as...
متن کاملLogistic discrimination using robust estimators
Logistic regression is frequently used for classifying observations into two groups. Unfortunately there are often outlying observations in a data set, who might affect the estimated model and the associated classification error rate. In this paper, the effect of observations in the training sample on the error rate is studied by computing influence functions. It turns out that the usual influe...
متن کاملSome Alternative Classes of Shrinkage Estimators for a Scale Parameter of the Exponential Distribution
This paper proposes some alternative classes of shrinkage estimators and analyzes their properties. In particular, some new shrinkage estimators are identified and compared with Pandey (1983), Pandey and Srivastava (1985) and Jani (1991) estimators. Numerical illustrations are also provided.
متن کاملShrinkage Estimators for High-Dimensional Covariance Matrices
As high-dimensional data becomes ubiquitous, standard estimators of the population covariance matrix become difficult to use. Specifically, in the case where the number of samples is small (large p small n) the sample covariance matrix is not positive definite. In this paper we explore some recent estimators of sample covariance matrices in the large p, small n setting namely, shrinkage estimat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Modern Applied Statistical Methods
سال: 2016
ISSN: 1538-9472
DOI: 10.22237/jmasm/1462077300