Inference in semiparametric binary response models with interval data

Inference in Semiparametric Dynamic Models for Binary Longitudinal Data

This article deals with the analysis of a hierarchical semiparametric model for dynamic binary longitudinal responses. The main complicating components of the model are an unknown covariate function and serial correlation in the errors. Existing estimation methods for models with these features are of O(N3), where N is the total number of observations in the sample. Therefore, nonparametric est...

Binary Regression With a Misclassified Response Variable in Diabetes Data

Objectives: The categorical data analysis is very important in statistics and medical sciences. When the binary response variable is misclassified, the results of fitting the model will be biased in estimating adjusted odds ratios.  The present study aimed to use a method to detect and correct misclassification error in the response variable of Type 2 Diabetes Mellitus (T2DM), applying binary ...

New DEA/Location Models with Interval Data

Recently the concept of facility efficiency, which defined by data envelopment analysis (DEA), introduced as a location modeling objective, that provides facilities location’s effect on their performance in serving demands. By combining the DEA models with the location problem, two types of “efficiencies” are optimized: spatial efficiency which measured by finding the least cost location and al...

Target setting in additive models with preferences and interval data

Semiparametric Inference and Models

We review semiparametric models and various methods of inference – efficient and inefficient – for such models.”