Distributions for Modeling Location Scale and Shape

<p>This is a book about statistical distributions their properties and their application to modelling the dependence of the location scale and shape of the distribution of a response variable on explanatory variables. It will be especially useful to applied statisticians and data scientists in a wide range of application areas and also to those interested in the theoretical properties of distributions. This book follows the earlier book ‘Flexible Regression and Smoothing: Using GAMLSS in R’ [Stasinopoulos et al. 2017] which focused on the GAMLSS model and software. GAMLSS (the Generalized Additive Model for Location Scale and Shape [Rigby and Stasinopoulos 2005]) is a regression framework in which the response variable can have any parametric distribution and <i>all </i>the distribution parameters can be modelled as linear or smooth functions of explanatory variables. The current book focuses on distributions and their application.</p><p></p><p><strong>Key features:</strong></p><p></p><ul> <br><br><p></p> <li>Describes over 100 distributions (implemented in the GAMLSS packages in R) including continuous discrete and mixed distributions.</li> <br><br> </ul><p></p><ul> <br><br><p></p> <li>Comprehensive summary tables of the properties of the distributions.</li> <br><br> </ul><p></p><ul> <br><br><p></p> <li>Discusses properties of distributions including skewness kurtosis robustness and an important classification of tail heaviness.</li> <br><br> </ul><p></p><ul> <br><br><p></p> <li>Includes mixed distributions which are continuous distributions with additional specific values with point probabilities.</li> <br><br> </ul><p></p><ul> <br><br><p></p> <li>Includes many real data examples with R code integrated in the text for ease of understanding and replication.</li> <br><br> </ul><p></p><ul> <br><br><p></p> <li>Supplemented by the gamlss website.</li> <br><br> </ul><p></p><p>This book will be useful for applied statisticians and data scientists in selecting a distribution for a univariate response variable and modelling its dependence on explanatory variables and to those interested in the properties of distributions.</p>