Helen Brown E. Applied Mixed Models in Medicine
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A fully updated edition of this key text on mixed models, focusing on applications in medical research The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and
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Brown Helen Dawes Applied Mixed Models in Medicine
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A mixed model allows the incorporation of both fixed and random variables within a statistical analysis. This enables efficient inferences and more information to be gained from the data. The application of mixed models is an increasingly popular way of analysing medical data, particularly in the
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Thomas Mathew Statistical Tests for Mixed Linear Models
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An advanced discussion of linear models with mixed or random effects. In recent years a breakthrough has occurred in our ability to draw inferences from exact and optimum tests of variance component models, generating much research activity that relies on linear models with mixed and random
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Paul Stewart W. Linear Model Theory
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A precise and accessible presentation of linear model theory, illustrated with data examples Statisticians often use linear models for data analysis and for developing new statistical methods. Most books on the subject have historically discussed univariate, multivariate, and mixed linear models
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Eugene Demidenko Mixed Models
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Praise for the First Edition “This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one’s personal library.” —Journal of the American Statistical Association Mixed modeling is a crucial area of statistics, enabling
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Alvin Rencher C. Linear Models in Statistics
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The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear
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Eva Cantoni Robust Methods in Biostatistics
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Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the
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Helen Brown E. Prospect Research for Fundraisers. The Essential Handbook
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Essential tools for implementing right-sized prospect research techniques that help nonprofit organizations reach their fundraising goals Written especially for front-line fundraisers, Prospect Research for Fundraisers presents a practical understanding of prospect research, prospect management,
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N. Galwey W. Introduction to Mixed Modelling. Beyond Regression and Analysis of Variance
Купить за 10067.99 RUR
Mixed modelling is very useful, and easier than you think! Mixed modelling is now well established as a powerful approach to statistical data analysis. It is based on the recognition of random-effect terms in statistical models, leading to inferences and estimates that have much wider applicability
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N. Galwey W. Introduction to Mixed Modelling. Beyond Regression and Analysis of Variance
Купить за 10519.8 RUR
Mixed modelling is very useful, and easier than you think! Mixed modelling is now well established as a powerful approach to statistical data analysis. It is based on the recognition of random-effect terms in statistical models, leading to inferences and estimates that have much wider applicability
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Nikolaos Limnios Introduction to Stochastic Models
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This book provides a pedagogical examination of the way in which stochastic models are encountered in applied sciences and techniques such as physics, engineering, biology and genetics, economics and social sciences. It covers Markov and semi-Markov models, as well as their particular cases:
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P. C. G. Vassiliou Discrete-time Asset Pricing Models in Applied Stochastic Finance
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Stochastic finance and financial engineering have been rapidly expanding fields of science over the past four decades, mainly due to the success of sophisticated quantitative methodologies in helping professionals manage financial risks. In recent years, we have witnessed a tremendous acceleration
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