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Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications

, Statistics of Diffusions, Markov Processes and other Statistical Applications.;

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Statistical Topics and Stochastic Models for Dependent Data with Applications

, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes;

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Dynamics of Statistical Experiments

methods for the operator and martingale characterization of Markov processes, including singular perturbation methods. Furthermore, they justify;

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High-dimensional Data Analysis

, feature selection, regression, classification, dimension reduction, as well as applications in survival analysis and biomedical research.The book;

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Stochastic Models, Statistics and Their Applications

contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival;

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Applied Statistical Science II

Contents: Nonparametric Perspectives in Regression Analysis; Record Values of Univariate Distribution; A Transfer Theorem for Multitype;

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The Exponential Distribution

applications of mixtures, and applications to survival analysis.;

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Extreme Value Modeling and Risk Analysis

Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events;

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Statistical Models and Methods for Reliability and Survival Analysis

Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as;

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Handbook of Regression Analysis With Applications in R

Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical;

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Survival Analysis in Medicine and Genetics

regression, multivariate survival data analysis, the sub-distribution method for competing risks data, the cure rate model, and Bayesian inference;

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Survival Analysis in Medicine and Genetics

regression, multivariate survival data analysis, the sub-distribution method for competing risks data, the cure rate model, and Bayesian inference;

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An Introduction to Generalized Linear Models

nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis Connects Bayesian analysis and MCMC;

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An Introduction to Generalized Linear Models

nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis Connects Bayesian analysis and MCMC;

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Sense and Nonsense of Statistical Inference: Controversy

epistemology; reveals fallacious practices in statistical causal inference, stressing the misuse of regression models and time-series analysis as;

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Sense and Nonsense of Statistical Inference

epistemology; reveals fallacious practices in statistical causal inference, stressing the misuse of regression models and time-series analysis as;

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Flowgraph Models For Multistate Time-To-Event Data

processes without prior knowledge of stochastic processes----opening the door to interesting applications in survival analysis and reliability as;

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An Introduction to Generalized Linear Models

multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian;

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Sequential Change Detection and Hypothesis Testing

, including in modern, complex information systems and networks. These extensions are illustrated using Markov, hidden Markov, state-space, regression;

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Sequential Change Detection and Hypothesis Testing

, including in modern, complex information systems and networks. These extensions are illustrated using Markov, hidden Markov, state-space, regression;

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Understanding Regression Analysis

explanations and applications of statistical significance (p-values), incorporating the American Statistical Association guidelines Use;

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Regression Models For Time Series Analysis

is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis;

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Statistical Inference for Piecewise-deterministic Markov Processes

-deterministic Markov processes offers a detailed and comprehensive survey of state-of-the-art results. It covers a wide range of general processes as;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Extremes In Random Fields

of increasingly complex data. The extreme values that emerge in the statistical analysis of complex data are often of particular interest. This book;

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Statistical Models For Causal Analysis

, logistic regression, multinomial logistic regression and survival models. The goal is to impart a working knowledge of these models with a minimum;

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