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Promotion Time Cure Model with Local Polynomial Estimation
In modeling survival data with a cure fraction, flexible modeling of covariate effects on the probability of cure has important medical implications,...
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Hierarchical Bayes small area estimation for county-level health prevalence to having a personal doctor
The complexity of survey data and the availability of data from auxiliary sources motivate researchers to explore estimation methods that extend...
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Three-fold Fay–Herriot model for small area estimation and its diagnostics
This paper introduces a three-fold Fay–Herriot model with random effects at three hierarchical levels. Small area best linear unbiased predictors of...
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Advances in the Use of Capture-Recapture Methodology in the Estimation of U.S. Census Coverage Error
A post-enumeration survey (PES) is an important tool for assessing the quality of a census and gaining information about how to improve census-taking...
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Bayesian Quantile Estimation in Deconvolution
Estimating quantiles of a population is a fundamental problem of high practical relevance in nonparametric statistics. This chapter addresses the...
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Population Based Search
This chapter introduces population based search methods and their R implementations, namely genetic and evolutionary algorithms, differential...
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On the choice of the optimal single order statistic in quantile estimation
We study the classical statistical problem of the estimation of quantiles by order statistics of the random sample. For fixed sample size, we...
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Population (Hierarchical) Models
Simple hierarchical (or population) models for multilevel data, in particular in the case of a multicenter study, are introduced. A hierarchical...
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Melded Integrated Population Models
Integrated population models provide a framework for assimilating multiple datasets to understand population dynamics. Understanding drivers of...
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Covariate-Balancing-Aware Interpretable Deep Learning Models for Treatment Effect Estimation
Estimating treatment effects is of great importance for many biomedical applications with observational data. Particularly, interpretability of the...
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Bayesian hierarchical spatial model for small-area estimation with non-ignorable nonresponses and its application to the NHANES dental caries data
The National Health and Nutrition Examination Survey (NHANES) is a major program of the National Center for Health Statistics, designed to assess the...
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Finite Population Survey Sampling: An Unapologetic Bayesian Perspective
This article attempts to offer some perspectives on Bayesian inference for finite population quantities when the units in the population are assumed...
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Calibrated Estimators for Population Means Using Standard Deviation of the Auxiliary Variable
In this paper, a new calibration constraint, using known standard deviation of the auxiliary variable was proposed for obtaining calibration...
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On Estimation of Stress-Strength Reliability with Zero-Inflated Poisson Distribution
Many real-world phenomena generate count data with inflated number of zeroes. To model such datasets, the zero-inflated Poisson model has been used...
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Estimation of poverty and inequality in small areas: review and discussion
Never better said, a correct diagnosis is crucial for patient recovery. In the eradication of poverty, which is the first of the sustainable...
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Second-order (s.o.) multi-stage fixed-width confidence interval (FWCI) estimation strategies for comparing location parameters from two negative exponential (NE) populations: illustrations with cancer data
We consider two negative exponential (NE) populations with unknown location parameters and unknown but unequal scale parameters. We develop fixed-width...
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R-estimation in linear models: algorithms, complexity, challenges
The main objective of this paper is to discuss selected computational aspects of robust estimation in the linear model with the emphasis on R -estimato...
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Point Estimation
Given a parameter of interest, such as a population mean μ or population proportion p, the objective of point estimation is to use a sample to...
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Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series
We propose a new solution under the Bayesian framework to simultaneously estimate mean-based asynchronous changepoints in spatially correlated...
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Targeted maximum likelihood estimation for causal inference in survival and competing risks analysis
Targeted maximum likelihood estimation (TMLE) provides a general methodology for estimation of causal parameters in presence of high-dimensional...
