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Estimating Determinants of Stage at Diagnosis of Breast Cancer Prevalence in Western Nigeria Using Bayesian Logistic Regression
Breast cancer is the most common cancer-affecting women globally, and the stage at diagnosis remains a key factor to the final outcome. Late stage at...
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A multiple imputation approach for the Cox–Aalen cure model with interval-censored data
Interval censored survival data, where the exact event time is only known to lie in an interval, is commonly encountered in practice. Furthermore,...
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A new quantile regression model with application to human development index
A new odd log-logistic unit omega distribution is defined and studied, and some of its structural properties are obtained. A quantile regression...
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Redundancy Analysis for Binary Data Based on Logistic Responses
Redundancy Analysis (RDA) is one of the many possible methods to extract and summarize the variation in a set of response variables that can be...
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Bayesian Prediction and Model Checking
Aspects of Bayesian prediction have been addressed in previous chapters. In particular, Chaps. 7 and...
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Optimal subsample selection for massive logistic regression with distributed data
With the emergence of big data, it is increasingly common that the data are distributed. i.e., the data are stored at many distributed sites...
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The Focused Information Criterion for Logistic Time Series Regression Models Under Locally Biased Estimating Functions
Statistical modeling of biomedical data arising from the cytologic samples collected during cancer trials often involves the analysis of binary time...
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Regression Model
The generalized maximum entropy model and minimum divergence estimation are examined in a framework of regression paradigm, which is one of the most...
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Hierarchical Model
We introduce the motivation of building a hierarchical model. For a dataset example, we implement different models, from a simple model to a...
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Estimation of parameters of logistic regression for two-stage randomized response technique
When a survey study is related to sensitive issues such as political orientation, sexual orientation, and income, respondents may not be willing to...
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Dimension reduction-based adaptive-to-model semi-supervised classification
This paper introduces a novel Dimension Reduction-based Adaptive-to-model Semi-supervised Classification method, specifically designed for scenarios...
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Logistic Regression Analysis
This chapter describes the logistic regression. It is a powerful statistical technique for examining the assumed causal relationships between a set...
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New Statistical Matching Method Using Multinomial Logistic Regression Model
Statistical matching techniques aim to build a dataset by combining different data sources. In recent years, matching techniques have been employed...
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Random coefficients integer-valued threshold autoregressive processes driven by logistic regression
In this article, we introduce a new random coefficients self-exciting threshold integer-valued autoregressive process. The autoregressive...
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High dimensional controlled variable selection with model-X knockoffs in the AFT model
Interpretability and stability are two important characteristics required for the application of high dimensional data in statistics. Although the...
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High-Dimensional Feature Selection for Logistic Regression Using Blended Penalty Functions
The datasets analysed, in a Biostatistics environment, are frequently high-dimensional and multicollinearity is expected due to the nature of the...
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Robust semiparametric inference for polytomous logistic regression with complex survey design
Analyzing polytomous response from a complex survey scheme, like stratified or cluster sampling is very crucial in several socio-economics...
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On the monotonicity of the residual heteroscedasticity item response model
The residual heteroscedasticity (RH) model is a recently popularized asymmetric model that aims to model complex item response behavior. In this...
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Instrumental variable estimation of causal effects with applying some model selection procedures under binary outcomes
In observational studies, unmeasured covariates are an important problem. In the presence of some unmeasured covariates, some instrumental variable...
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Frequentist model averaging in the generalized multinomial logit model
The generalized multinomial logit (GMNL) model accommodates scale heterogeneity to the random parameters logit (RPL) model. It has been often used to...
