Doubly penalized likelihood estimator in heteroscedastic regression
نویسندگان
چکیده
منابع مشابه
Penalized Likelihood Functional Regression
This paper studies the generalized functional linear model with a scalar response and a functional predictor. The response given the functional predictor is assumed to come from the distribution of an exponential family. A penalized likelihood approach is proposed to estimate the unknown intercept and coefficient function in the model. Inference tools such as point-wise confidence intervals of ...
متن کاملAsymptotic properties of a double penalized maximum likelihood estimator in logistic regression
Maximum likelihood estimates in logistic regression may encounter serious bias or even non-existence with many covariates or with highly correlated covariates. In this paper, we show that a double penalized maximum likelihood estimator is asymptotically consistent in large samples. r 2007 Elsevier B.V. All rights reserved.
متن کاملGeneralized Nonparametric Regression via Penalized Likelihood
We consider the asymptotic analysis of penalized likelihood type estimators for generalized non-parametric regression problems in which the target parameter is a vector valued function defined in terms of the conditional distribution of a response given a set of covariates, A variety of examples including ones related to generalized linear models and robust smoothing are covered by the theory. ...
متن کاملMaximum Weighted Likelihood Estimator in Logistic Regression
The least weighted squares estimator is a well known technique in robust regression. Its likelihood analogy in logistic regression is the maximum weighted likelihood estimator, proposed in Vandev and Neykov (1998) and Mueller and Neykov (2003). This article mentions already proved properties, shows its inconsistency and compare it to the other estimators by an extensive simulation. Introduction...
متن کاملPenalized Maximum Likelihood Estimator for Skew Normal Mixtures
Skew normal mixture models provide a more flexible framework than the popular normal mixtures for modelling heterogeneous data with asymmetric behaviors. Due to the unboundedness of likelihood function and the divergency of shape parameters, the maximum likelihood estimators of the parameters of interest are often not well defined, leading to dissatisfactory inferential process. We put forward ...
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: Statistics & Probability Letters
سال: 2004
ISSN: 0167-7152
DOI: 10.1016/j.spl.2004.03.009