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Nonlinear Statistical Models

. The three major categories of statistical models that relate dependent variables to explanatory variables are covered: univariate regression;

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Explanatory Item Response Models

in psychology, education, and other social science areas) from the viewpoint of the statistical theory of generalized linear and nonlinear mixed models;

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Nonlinear Statistical Modeling

selection models, (ii) nonparametric and semi-parametric approaches to qualitative and sample selection models, and (iii) nonlinear estimation;

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Modelling Nonlinear Economic Relationships

This volume explains recent theoretical developments in the econometric modelling of relationships between different statistical series;

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Bayesian Methods for Nonlinear Classification and Regression

Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of nonlinear modelling and Bayesian;

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Confidence Intervals in Generalized Regression Models

generalized regression model (GRM)-of various types of regression models. It also uses a likelihood-based approach for performing statistical;

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Confidence Intervals in Generalized Regression Models

generalized regression model (GRM)-of various types of regression models. It also uses a likelihood-based approach for performing statistical;

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

models, robustness of relevant statistical methods, estimation of variance components appearing in linear models, generalizations to nonlinear;

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Nonlinear Models For Economic Decision Processes

, nonlinear dynamics and econometrics, physics of risk, statistical and probabilistic methods in economics and finance.This publication concentrates;

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Partially Linear Models

applications of partially linear regression techniques to various statistical problems. These problems include least squares regression, asymptotically;

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Non-Linear Time Series

This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book;

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Alternative Methods of Regression

. --Short Book Reviews of the International Statistical Institute In this introduction to nonlinear modeling, the authors examine a wide range;

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Nonlinear Time Series Analysis

in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and;

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Dose-Response Analysis Using R

-response data. The statistical models used are partly special cases, partly extensions of nonlinear regression models, generalized linear and;

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Dose-Response Analysis Using R

estimation and interpretation of entirely parametric nonlinear dose-response models using the powerful statistical environment R. Specifically, this;

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Recent Advances In Statistical Methods, Proceedings Of Statistics 2001 Canada

This volume consists of research papers dealing with computational and methodological issues of statistical methods on the cutting edge;

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Nonlinear Dynamics and Statistical Theories for Basic Geophysical Flows

, information theory, nonlinear dynamics and equilibrium statistical mechanics are introduced and applied to large time-selective decay, the effect;

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Principles And Practice Of Structural Equation Modelling

). Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory;

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Nonlinear Mathematics for Uncertainty and its Applications

, set-valued and fuzzy stochastic processes, imprecise probability theory and related statistical models, fuzzy mathematics, nonlinear;

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Environmental and Ecological Statistics with R

. It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression;

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Environmental and Ecological Statistics with R

then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees;

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Nonlinear Time Series

Designed for researchers and students, Nonlinear Times Series: Theory, Methods and Applications with R Examples familiarizes readers with;

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Frontiers In Statistics

-sampling methodologies, statistical computing, generalized nonlinear regression and mixed effects models, martingale transform tests for model;

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Frontiers In Statistics

-sampling methodologies, statistical computing, generalized nonlinear regression and mixed effects models, martingale transform tests for model;

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Time Series Modeling of Neuroscience Data

statistical identification method of highly nonlinear dynamical systems such as the Hodgkin-Huxley model, Lorenz chaos model, Zetterberg Model, and;

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Nonparametric Statistical Methods Using R

nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the;

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Nonparametric Statistical Methods Using R

general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation;

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