Properties of estimators of parameters in logistic regression models

Properties of various types of estimators of the regression coefficients in linear logistic regression models are considered. The estimators include those based on maximum likelihood, minimum chi-square and weighted least squares. Theoretical approximations to the biases of the estimators are developed. The results of a large scale simulation investigation evaluating the moment properties of the estimators are presented for the case of a logistic model with a single explanatory variable

The extreme residuals in logistic regression models

Goodness of fit tests for logistic regression models using extreme residuals are considered. Moment properties of the Pearson residuals are developed and used to define modified residuals, for the cases when the model fit is made by maximum likelihood, minimum chi-square and weighted least squares. Approximations to the critical values of the extreme statistics based on the ordinary and modified Pearson residuals are developed and assessed for the case when the logistic regression model has a single explanatory variable

Null distribution of some goodness of fit statistics for logistic regression

The null distribution moment and percentile properties of several goodness of fit statistics for logistic regression models are considered. Small sample approximations to the critical values of the statistics are evaluated for the case of a single explanatory variable with equally spaced values

A Comprehensive Review of Deep Learning Architectures for Computer Vision Applications

The emergence of machine learning in the artificial intelligence field led the world of technology to make great strides. Today’s advanced systems with the ability of being designed just like human brain functions has given practitioners the ability to train systems so that they could process, analyze, classify, and predict different data classes. Therefore, the machine learning field has become a hot topic for scientists and researchers to introduce the best network with the highest performance for such mentioned purposes. In this article, computer vision science, image classification implementation, and deep neural networks are presented. This article discusses how models have been designed based on the concept of the human brain. The development of a Convolutional Neural Network (CNN) and its various architectures, which have shown great efficiency and evaluation in object detection, face recognition, image classification, and localization, are also introduced. Furthermore, the utilization and application of CNNs, including voice recognition, image processing, video processing, and text recognition, are examined closely. A literature review is conducted to illustrate the significance and the details of Convolutional Neural Networks in various applications