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A New Lifetime Model for Non-Monotone Failure Rate Data
Lifetime study of organisms and systems plays an important role in reliability theory and survival analysis. Lifetime models with a non-monotone...
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Nonparametric estimation for competing risks survival data subject to left truncation and interval censoring
In this article, we consider nonparametric estimation of the cumulative incidence function (CIF) for left-truncated and interval-censored competing...
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Survival Analysis for the Inverse Gaussian Distribution: Natural Conjugate and Jeffrey’s Priors
This study focuses on the use of a Bayesian method to analyze survival data that follow an inverse Gaussian (IG) distribution. Both IG parameters are...
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The Application of Survival Analysis Methods in the Examination of Foreign Divestment in Poland
Investing is a process which takes time and can have an initial and an end phase. The same applies to foreign direct investment (FDI), which may...
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First Theory of Cancer Gene Data Analysis by 169 Microarrays—Four Universal Data Structures of Discriminant Data
The new discriminant theory (Theory1) analyzed six microarrays having two classes. The Revised IP Optimal LDF (RIP) finds the minimum number of...
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Tests of stochastic dominance with repeated measurements data
The paper explores a testing problem which involves four hypotheses, that is, based on observations of two random variables X and Y , we wish to...
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Causal survival analysis under competing risks using longitudinal modified treatment policies
Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend...
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Efficient particle smoothing for Bayesian inference in dynamic survival models
This article proposes an efficient Bayesian inference for piecewise exponential hazard (PEH) models, which allow the effect of a covariate on the...
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The expectation–maximization approach for Bayesian additive Cox regression with current status data
In this paper, we propose a Bayesian additive Cox model for analyzing current status data based on the expectation–maximization variable selection...
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Statistical Modelling for Big and Little Data
While the difference between “Data Science” and “Statistics” disciplines is, at best, blurred, many people associate machine learning methods and big...
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Data Visualization
When light is reflected off of a stimulus, it is captured by the eye. However, most processing is done by the brain, which we think of as visual...
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Functional Causal Inference with Time-to-Event Data
Functional data analysis has proven to be a powerful tool for capturing and analyzing complex patterns and relationships in a variety of fields,...
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Equivalence tests for the difference of two survival functions under the class of Box–Cox transformation model
Establishing equivalence of two treatments has received a lot attention in the pharmaceutical industry. For assessing equivalence of two survival...
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Semiparametric predictive inference for failure data using first-hitting-time threshold regression
The progression of disease for an individual can be described mathematically as a stochastic process. The individual experiences a failure event when...
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Statistical inference for Cox model under case-cohort design with subgroup survival information
With the explosive growth of data, it is a challenge to infer the quantity of interest by combining the existing different research data about the...
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Survival Analysis and Cox Regression for Time-to-Event Data
In Chap. 5 , we learned the methods to confirm a potential X~Y relationship with multiple linear regression...
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The XGTDL family of survival distributions
Non-PH parametric survival modelling is developed within the framework of the multiple logistic function. The family considered comprises three basic...
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Binary Data
Many of the results derived under the assumption that observations are continuously distributed extend to dichotomous and categorical responses....
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Variable selection in proportional odds model with informatively interval-censored data
The proportional odds (PO) model is one of the most commonly used models for regression analysis of failure time data in survival analysis. It...
