Nick Bingham  | Springer

66,480원  | 20101102  | 9781848829688

Regression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error. The subject is inherently two- or higher- dimensional, thus an understanding of Statistics in one dimension is essential. Regression: Linear Models in Statistics fills the gap between introductory statistical theory and more specialist sources of information.

Rencher, Alvin C.  | Wiley

84,000원  | 20080101  | 9780471754985

This completely revised and updated edition develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models.

 | John Wiley & Sons Inc

343,310원  | 20080314  | 9780470388044

This set contains: 9780471221746 SAS for Linear Models, Fourth Edition by Ramon Littell, Walter W. Stroup, Rudolf Freund and 9780471754985 Linear Models in Statistics, Second Edition by Alvin C. Rencher, G. Bruce Shaalje.

 | Springer Verlag

110,230원  | 20161021  | 9783319345697

This textbook is an approachable introduction to statistical analysis using matrix algebra.

 | CRC Press

96,160원  | 20240527  | 9780367680442

For advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II and courses in Generalized Linear Models, Longitudinal Data Analysis, Correlated Data, Multilevel Models. Material on R at the end of each chapter. Solutions manual for qualified instructors.

 | Springer Nature B.V.

69,980원  | 20190710  | 9781441901194

This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed.

Rader, David J.  | Wiley

144,000원  | 20100826  | 9780470484517

Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering.

Knight, John/ Satchell, Stephen  | Butterworth-Heinemann

249,230원  | 20050128  | 9780750660068

The determination of the values of stocks, bonds, options, futures, and derivatives is done by the scientific process of asset pricing, which has developed due to advances in financial theory and econometrics. This book covers the science of asset pricing by concentrating on the most widely used modelling technique called: Linear Factor Modelling.

Franses, Philip Hans/ Dijk, Dick Van  | Cambridge

341,250원  | 20001001  | 9780521770415

An accessible guide to one of the fastest growing areas in financial analysis by one of Europes's leading teaching and researching teams, first published in 2000. This classroom-tested advanced undergraduate and graduate textbook provides an in-depth treatment of non-linear models, including regime-switching and artificial neural networks.

 | Lightning Source

277,870원  | 20210101  | 9780849396717

This text stands apart from other books on experiment design. It presents theory and methods, emphasizes both the design selection for an experiment and the analysis of data, and integrates the analysis for the various designs with the general the...

Koul, H. L./ Koul, H. L. Weighted Empiricals and  | Springer

96,230원  | 20020901  | 9780387954769

This book presents a unified approach for obtaining the limiting distributions of minimum distance, M and R estimators corresponding to non-smooth underlying scores in a large class of dynamic non-linear models including ARCH models. It also discusses classes of goodness-of-t tests for fitting an error distribution in some of these models and/or fitting a regression-autoregressive function without assuming the knowledge of the error distribution. The main tool is the asymptotic equicontinuity of certain basic weighted residual empirical processes in the uniform and L2 metrics. The contents of this monograph should be useful to graduate students and research scholars in statistics, econometrics, and finance. This book is a an updated edition of the author's monograph Weighted Empirical Processes and Liner Models (IMS Lecture Notes-Monograph 21, 1992). The new edition differs from the previous one in many ways. To mention just a few: It includes asymptotically distribution free tests for fitting a regression and/or an autoregressive models; the asymptotic distributions of auto-regression quantiles and rank scores; and above all the weak convergence of the residual empirical processes useful in nonlinear ARCH models. Hira L. Koul is a professor of statistics at Michigan State University. He is a Fellow of the IMS and an Elected Member of the International Statistical Institute. He was awarded the prestigious Humboldt Research Award for Senior Researchers in 1995. He has been on the editorial boards of the Annals of Statistics, Sankhya, and J. Indian Statistical Association. Currently he is a Coordinating Editor of the Journal of Statistical Planning and Inference, and an Associate Editor of Statistics and Probability Letters.

Franses, Philip Hans/ Dijk, Dick Van  | Cambridge

157,500원  | 20001001  | 9780521779654

This is the most up-to-date and accessible guide to one of the fastest growing areas in financial analysis by two of the most accomplished young econometricians in Europe. This classroom-tested advanced undergraduate and graduate textbook provides...

Lindsey, J.K.  | Springer

174,980원  | 20000420  | 9780387982182

Applying Generalized Linear Modelsdescribes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference. Many students, even in relatively advanced statistics courses, do not have an overview whereby they can see that the three areas - linear normal, categorical, and survival models - have much in common. The author shows the unity of many of the commonly used models and provides the reader with a taste of many different areas, such as survival models, time series, and spatial analysis. This book should appeal to applied statisticians and to scientists with a basic grounding in modern statistics. With the many exercises included at the ends of chapters, it will be an excellent text for teaching the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, and should be familiar at least with the analysis of the simpler normal linear models, regression and ANOVA. The author is professor in the biostatistics department at Limburgs University, Diepenbeek, in the social science department at the University of Lige, and in medical statistics at DeMontfort University, Leicester. He is the author of nine other books.

Hjortso, Martin Aksel, Wolenski, Peter R.  | World Scientific Publishing Europe Ltd

241,500원  | 20180905  | 9789813270879

Mathematics remains a core area of engineering. Formulating and analyzing mathematical models of basic engineering systems is an essential skill that all engineering students should endeavor to acquire.This book will serve as an excellent introduction to linear mathematics for engineering students, both seniors and graduate students. It is the result of a collaboration between a chemical engineer and a mathematician, both of whom have taught classes on modelling and applied mathematics.

Thomas W. Yee  | Springer Verlag

251,980원  | 20150914  | 9781493928170

This book treats distributions and classical models as generalized regression models, and the result is a much broader application base for GLMs and GAMs. The book can be used in senior undergraduate or first-year postgraduate courses on GLMs or categorical data analysis and as a methodology resource for VGAM users.