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Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through numerous examples and case studies. The most important definitions and statements are concisely summarized in boxes, and the underlying data sets and code are available online on the book’s dedicated website. Availability of (user-friendly) software has been a major criterion for the methods selected and presented.The chapters address the classical linear model and its extensions, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression and distributional regression models. Two appendices describe the required matrix algebra, as well as elements of probability calculus and statistical inference.In this substantially revised and updated new edition the overview on regression models has been extended, and now includes the relation between regression models and machine learning, additional details on statistical inference in structured additive regression models have been added and a completely reworked chapter augments the presentation of quantile regression with a comprehensive introduction to distributional regression models. Regularization approaches are now more extensively discussed in most chapters of the book.The book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written at an intermediate mathematical level and assumes only knowledge of basic probability, calculus, matrix algebra and statistics.

Statistical science --- Mathematical statistics --- statistiek --- statistisch onderzoek --- Regression analysis. --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Anàlisi de regressió --- Model de regressió --- Regressió lineal --- Estadística --- Estadística matemàtica --- Correlació múltiple (Estadística) --- Correlació (Estadística) --- Models d'equacions estructurals

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This book presents a general method for deriving higher-order statistics of multivariate distributions with simple algorithms that allow for actual calculations. Multivariate nonlinear statistical models require the study of higher-order moments and cumulants. The main tool used for the definitions is the tensor derivative, leading to several useful expressions concerning Hermite polynomials, moments, cumulants, skewness, and kurtosis. A general test of multivariate skewness and kurtosis is obtained from this treatment. Exercises are provided for each chapter to help the readers understand the methods. Lastly, the book includes a comprehensive list of references, equipping readers to explore further on their own.

Anàlisi multivariable --- Anàlisi multivariant --- Estadística matemàtica --- Matrius (Matemàtica) --- Anàlisi de conglomerats --- Anàlisi de correspondències (Estadística) --- Anàlisi discriminant --- Modelització multiescala --- Models d'equacions estructurals --- Anàlisi conjunt (Màrqueting) --- Multivariate analysis. --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- Statistics. --- Statistical Theory and Methods. --- Statistics and Computing. --- Data processing. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics

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Multivariate statistical analysis has undergone a rich and varied evolution during the latter half of the 20th century. Academics and practitioners have produced much literature with diverse interests and with varying multidisciplinary knowledge on different topics within the multivariate domain. Due to multivariate algebra being of sustained interest and being a continuously developing field, its appeal breaches laterally across multiple disciplines to act as a catalyst for contemporary advances, with its core inferential genesis remaining in that of statistics. It is exactly this varied evolution caused by an influx in data production, diffusion, and understanding in scientific fields that has blurred many lines between disciplines. The cross-pollination between statistics and biology, engineering, medical science, computer science, and even art, has accelerated the vast amount of questions that statistical methodology has to answer and report on. These questions are often multivariate in nature, hoping to elucidate uncertainty on more than one aspect at the same time, and it is here where statistical thinking merges mathematical design with real life interpretation for understanding this uncertainty. Statistical advances benefit from these algebraic inventions and expansions in the multivariate paradigm. This contributed volume aims to usher novel research emanating from a multivariate statistical foundation into the spotlight, with particular significance in multidisciplinary settings. The overarching spirit of this volume is to highlight current trends, stimulate a focus on, and connect multidisciplinary dots from and within multivariate statistical analysis. Guided by these thoughts, a collection of research at the forefront of multivariate statistical thinking is presented here which has been authored by globally recognized subject matter experts.

Mathematics. --- Anàlisi multivariable --- Math --- Science --- Anàlisi multivariant --- Estadística matemàtica --- Matrius (Matemàtica) --- Anàlisi de conglomerats --- Anàlisi de correspondències (Estadística) --- Anàlisi discriminant --- Modelització multiescala --- Models d'equacions estructurals --- Anàlisi conjunt (Màrqueting) --- Statistics. --- Applied Statistics. --- Statistical Theory and Methods. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics

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Consumers --- Marketing research --- Neural networks (Computer science). --- Research --- Data processing. --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Market research --- Marketing --- Markets --- Research, Industrial --- Customers (Consumers) --- Shoppers --- Persons --- Neural networks (Computer science) --- Consumidors --- Màrqueting --- Processament de dades --- Xarxes neuronals (Informàtica) --- Models d'equacions estructurals --- Equacions estructurals --- Mètodes d'equacions estructurals --- SEM (Structural equation modeling) --- Anàlisi multivariable --- Correlació (Estadística) --- Anàlisi de regressió --- Anàlisi factorial --- Xarxes neurals (Informàtica) --- Xarxes neurals artificials --- Xarxes neuronals artificials --- Cibernètica --- Informàtica tova --- Intel·ligència artificial --- Computació evolutiva --- Xarxes neuronals convolucionals --- Intel·ligència computacional --- Processament de dades electròniques --- Processament automàtic de dades --- Processament electrònic de dades --- Processament integrat de dades --- Sistematització de dades (Ordinadors) --- Tractament de dades --- Tractament electrònic de dades --- Tractament integrat de dades --- Automatització --- Informàtica --- Complexitat computacional --- Curació de dades --- Depuració (Informàtica) --- Estructures de dades (Informàtica) --- Gestió de bases de dades --- Informàtica mòbil --- Informàtica recreativa --- Sistemes en línia --- Temps real (Informàtica) --- Tractament del llenguatge natural (Informàtica) --- Processament òptic de dades --- Protecció de dades --- Transmissió de dades --- Tolerància als errors (Informàtica) --- Comercialització --- Anàlisi conjunt (Màrqueting) --- Cicle de vida del producte --- Codificació de productes --- Construcció de marca (Màrqueting) --- Estudis de mercat --- Diferenciació de productes --- Fidelització dels clients --- Llotges de comerç --- Màrqueting bancari --- Màrqueting ecològic --- Màrqueting internacional --- Màrqueting de relacions --- Màrqueting directe --- Màrqueting industrial --- Màrqueting per Internet --- Màrqueting social --- Màrqueting territorial --- Màrqueting turístic --- Marxandatge --- Política de preus --- Productes nous --- Ciències empresarials --- Gestió de vendes --- Venda --- Clients --- Compradors --- Compres --- Persones --- Adolescents consumidors --- Conducta dels consumidors --- Cooperativisme --- Enquestes de consum --- Infants consumidors --- Protecció del consumidor --- Relacions amb els clients --- Cooperatives de consum --- Serveis d'atenció al client --- Màrqueting.

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This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience, Statistical Regression Modeling promotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields.

Regression analysis. --- R (Computer program language) --- GNU-S (Computer program language) --- Domain-specific programming languages --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Anàlisi de regressió --- R (Llenguatge de programació) --- GNU-S (Llenguatge de programació) --- Llenguatges de programació --- Model de regressió --- Regressió lineal --- Estadística --- Estadística matemàtica --- Correlació múltiple (Estadística) --- Correlació (Estadística) --- Models d'equacions estructurals --- Statistics. --- Programming languages (Electronic computers). --- Statistical Theory and Methods. --- Applied Statistics. --- Programming Language. --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Electronic data processing --- Languages, Artificial --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics