Flexible Bayesian Regression Modelling

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Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample …
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    Om Flexible Bayesian Regression Modelling

    Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for considerable flexibility in modeling techniques. It reviews three forms of flexibility: methods which provide flexibility in their error distribution; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be flexible (such as spline models). Each chapter discusses the key aspects of fitting a regression model. R programs accompany the methods. This book is particularly relevant to non-specialist practitioners with intermediate mathematical training seeking to apply Bayesian approaches in economics, biology, finance, engineering and medicine.Introduces powerful new nonparametric Bayesian regression techniques to classically trained practitionersFocuses on approaches offering both superior power and methodological flexibilitySupplemented with instructive and relevant R programs within the textCovers linear regression, nonlinear regression and quantile regression techniques Provides diverse disciplinary case studies for correlation and optimization problems drawn from Bayesian analysis 'in the wild'

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    Detaljer

    Format
    E-Bok
    Kopisperre
    Teknisk DRM
    Filformat
    ePUB
    Utgivelsesår
    2019
    Forlag
    Elsevier Science
    Språk
    Engelsk
    ISBN
    9780128158630
    Sider
    302
    Emne
    Bayesian inference, Business mathematics & systems, Econometrics, Mathematical modelling

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