In this paper, we proposed an adaptive robust variable selection procedure for the logistic regression model. The method is to outliers and considers goodness-of-fit of Furthermore, apply MM algorithm solve optimization problem. Monte Carlo studies are evaluated finite-sample performance method. results show that when there in dataset or distribution covariate deviates from normal distribution,...

The biuret method for total protein has been compared on the Boehringer Mannheim Diagnostics (BMD) Hitachi 717 Analyzer with a candidate reference method in efforts to standardize the BMD method. The methods were compared using 115 paired serum specimens collected during morning rounds at the Roger Williams Hospital. Results from the test method were compared to the reference method using a sta...

We discuss prediction of random effects and of expected responses in multilevel generalized linear models. Prediction of random effects is useful for instance in small area estimation and disease mapping, effectiveness studies and model diagnostics. Prediction of expected responses is useful for planning, model interpretation and diagnostics. For prediction of random effects, we concentrate on ...

Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications from the social to the physical sciences and beyond. Real data, however, are often overor under-dispersed and, thus, not conducive to Poisson regression. We propose a regression model based on the Conway–Maxwell-Poisson (COM-Poisson) distribution to address this problem. The COM-Poisson r...

1. Overview We present a set of useful diagnostics for checking model assumptions and model fit for a type of mixed model for longitudinal data, random coefficient models. Because of the increasing use of complex sampling designs in longitudinal studies, there is a need for diagnostics that can be used with data with unequal probabilities of selection. In particular, the usual diagnostics, both...