Regression models with mixed sampling frequencies

Quantile Regression for Mixed Models

Cardiometabolic diseases have substantially increased in China in the past 20 years and blood pressure is a primary modifiable risk factor. Using data from the China Health and Nutrition Survey we examine blood pressure trends in China from 1991 to 2009, with a concentration on age cohorts and urbanicity. Very large values of blood pressure are of interest, so we model the conditional quantile ...

Factors Affecting Hospital Length of Stay Using Mixed Poisson Regression Models

Background and purpose: Modeling of Hospital Length of Stay (LOS) is of great importance in healthcare systems, but there is paucity of information on this issue in Iran. The aim of this study was to identify the optimal model among different mixed poisson distributions in modeling the LOS and effective factors. Materials and methods: In this cross-sectional study, we studied 1256 records, inc...

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

Sampling from the posterior distribution in generalized linear mixed models

Regression Analysis with Response-selective Sampling

Response-selective sampling, in which samples are drawn from a population according to the values of the response variable, is common in biomedical, epidemiological, economic and social studies. This paper proposes to use transformation models, known as the generalized accelerated failure time model in econometrics, for regression analysis with response-selective sampling. With unknown error di...