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Mathematical and Statistical Estimation Approaches in Epidemiology

Mathematical and Statistical Estimation Approaches in Epidemiology compiles t- oretical and practical contributions of experts in the;

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Modern Methods for Epidemiology

point for those who are new to epidemiology, and for those looking for guidance in more modern statistical approaches to observational;

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Statistical and Mathematical Sciences and Their Applications

Discusses interesting topics in statistical and mathematical sciences and their applications authored by renowned statisticians and;

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Visualizing Statistical Models And Concepts

Examines classic algorithms, geometric diagrams, and mechanical principles for enhancing visualization of statistical estimation procedures;

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Visualizing Statistical Models And Concepts

Examines classic algorithms, geometric diagrams, and mechanical principles for enhancing visualization of statistical estimation procedures;

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Statistical Inference

Adopting a broad view of statistical inference, this text concentrates on what various techniques do, with mathematical proofs kept to a;

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Mathematical Statistics

Presents a unified approach to parametric estimation, confidence intervals, hypothesis testing, and statistical modeling, which are;

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Statistical Thinking in Epidemiology

in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research;

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Statistical Thinking in Epidemiology

in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research;

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Introduction to Mathematical Statistics, Global Edition

exercises. Classical statistical inference procedures in estimation and testing are explored extensively, and the text's flexible organisation makes;

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Mathematical Tools for Understanding Infectious Disease Dynamics

latest research in mathematical modeling of infectious disease epidemiology * Integrates deterministic and stochastic approaches * Teaches;

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Biostatistics for Human Genetic Epidemiology

data analysis. It examines probabilistic and statistical theories for modeling human genetic epidemiology - leading the readers;

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Mathematical Foundations of Infinite-Dimensional Statistical Models

theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm;

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Statistics for Epidemiology

Statistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret;

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Quantitative Methods for HIV/AIDS Research

development; the final section focuses on statistical issues in the mathematical modeling of HIV/AIDS pathogenesis, treatment and epidemiology;

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Quantitative Methods for HIV/AIDS Research

development; the final section focuses on statistical issues in the mathematical modeling of HIV/AIDS pathogenesis, treatment and epidemiology;

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Statistical Testing Strategies in the Health Sciences

Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making, ranging from;

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Statistical Testing Strategies in the Health Sciences

Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making, ranging from;

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Proceedings of the International Conference on Linear Statistical Inference Linstat '93

This volume contains a selection of invited and contributed papers presented at the International Conference on Linear Statistical;

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Computational Phylogenetics

A comprehensive account of both basic and advanced material in phylogeny estimation, focusing on computational and statistical issues. No;

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Statistical Models and Causal Inference

epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates;

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A Course on Small Area Estimation and Mixed Models

This advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers;

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Mathematical Statistics

fundamental concepts in statistical decision theory and inference. Subsequent chapters contain detailed studies on some important topics: unbiased;

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Statistical Models & Causal Inference

epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates;

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Sparse Estimation with Math and R

experience. This textbook approaches the essence of sparse estimation by considering math problems and building R programs. Each chapter introduces;

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Sparse Estimation with Math and Python

experience. This textbook approaches the essence of sparse estimation by considering math problems and building Python programs. Each chapter;

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Statistical Reliability Engineering

field, Statistical Reliability Engineering: Covers all mathematical models for statistical reliability analysis, including Bayesian estimation;

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