Fitting linear regression models to censored data by least squares and maximum likelihood methods

1986 ◽  
Vol 15 (11) ◽  
pp. 3227-3243 ◽  
Author(s):  
S. Chatterjee ◽  
D.L. McLeish
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Least Squares ◽  
Censored Data ◽  
Regression Models ◽  
Linear Regression Models ◽  
Likelihood Methods ◽  
Maximum Likelihood Methods

On Consistency of Estimators in Simple Linear Regression Models

2002 ◽  
Vol 53 (3-4) ◽  
pp. 261-264 ◽  
Author(s):  
Anindya Roy ◽  
Thomas I. Seidman
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Regression Models ◽  
Sufficient Condition ◽  
Simple Linear Regression ◽  
Linear Regression Models ◽  
Least Squares Estimators

We derive a property of real sequences which can be used to provide a natural sufficient condition for the consistency of the least squares estimators of slope and intercept for a simple linear regression models.

scholarly journals QML Estimators in Linear Regression Models with Functional Coefficient Autoregressive Processes

2010 ◽  
Vol 2010 ◽  
pp. 1-30 ◽  
Author(s):  
Hongchang Hu
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Regression Models ◽  
Asymptotic Properties ◽  
Asymptotic Distributions ◽  
Autoregressive Processes ◽  
Linear Regression Models ◽  
Unknown Parameters ◽  
Quasi Maximum Likelihood ◽  
Functional Coefficient

This paper studies a linear regression model, whose errors are functional coefficient autoregressive processes. Firstly, the quasi-maximum likelihood (QML) estimators of some unknown parameters are given. Secondly, under general conditions, the asymptotic properties (existence, consistency, and asymptotic distributions) of the QML estimators are investigated. These results extend those of Maller (2003), White (1959), Brockwell and Davis (1987), and so on. Lastly, the validity and feasibility of the method are illuminated by a simulation example and a real example.

scholarly journals Methods for Stratified Cluster Sampling with Informative Stratification

2007 ◽  
Vol 2007 ◽  
pp. 1-12
Author(s):  
Alastair Scott ◽  
Chris Wild
Keyword(s):  
Maximum Likelihood ◽  
Regression Models ◽  
Population Distribution ◽  
Estimating Equation ◽  
Cluster Sampling ◽  
Likelihood Methods ◽  
Weighted Estimating Equation ◽  
The Family ◽  
Maximum Likelihood Methods ◽  
Using Data

We look at fitting regression models using data from stratified cluster samples when the strata may depend in some way on the observed responses within clusters. One important subclass of examples is that of family studies in genetic epidemiology, where the probability of selecting a family into the study depends on the incidence of disease within the family. We develop the survey-weighted estimating equation approach for this problem, with particular emphasis on the estimation of superpopulation parameters. Full maximum likelihood for this class of problems involves modelling the population distribution of the covariates which is simply not feasible when there are a large number of potential covariates. We discuss efficient semiparametric maximum likelihood methods in which the covariate distribution is left completely unspecified. We further discuss the relative efficiencies of these two approaches.

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