The GLIM System
The GLIM System

Author: Brian Francis

Publisher: Oxford University Press, USA

Published: 1993

Total Pages: 848

ISBN-13:

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In statistics, fitting linear models to data is a general theme. This manual describes how GLIM 4--the popular software package--may be used for statistical analysis, including data manipulation and display, model fitting, and prediction. The manual has been divided into three distinct guides. The User Guide introduces and illustrates all the facilities in GLIM 4. Each chapter describes the directives relevant to a particular type of activity involved in the statistical modelling of data. The Modelling Guide presents a broad array of examples which comprise an effective introduction for new users. The Reference Guide contains a formal description of the syntax and semantics of the GLIM 4 language, of the data structures it handles, and of the directives provided, constituting a reference manual for the experienced user. This book is sure to be useful to research statisticians wherever GLIM is used.

Introducing Quantitative Geography
Introducing Quantitative Geography

Author: Larry O'Brien

Publisher: Routledge

Published: 2005-10-09

Total Pages: 380

ISBN-13: 1134987803

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The purpose of quantitative geography is to train geographers in numeracy and in the vital skills of data collection, processing and interpretation. Introducting Quantitative Geography describes quantification from first principles to cover all the key elements of quantitative geography. No previous knowledge of statistical procedures is assumed. Worked examples and computer analyses are used to explain measurement, scale, description, models and modelling. Building on this, the book explores and clarifies the intellectual and practical problems presented by numerical and technological advances in the field.

Statistical Modelling in GLIM
Statistical Modelling in GLIM

Author: Murray A. Aitkin

Publisher: Oxford University Press

Published: 1989

Total Pages: 390

ISBN-13: 9780198522034

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The analysis of data by statistical modelling is becoming increasingly important. This book presents both the theory of statistical modelling with generalized linear models and the application of the theory to practical problems using the widely available package GLIM. The authors have takenpains to integrate the theory with many practical examples which illustrate the value of interactive statistical modelling. Throughout the book theoretical issues of formulating and simplifying models are discussed, as are problems of validating the models by the detection of outliers and influential observations. The book arises from short courses given at the University of Lancaster's Centre for Applied Statistics, with an emphasis on practical programming in GLIM and numerous examples. A wide range of case studies is provided, using the normal, binomial, Poisson, multinomial, gamma, exponential andWeibull distributions. A feature of the book is a detailed discussion of survival analysis. Statisticians working in a wide range of fields, including biomedical and social sciences, will find this book an invaluable desktop companion to aid their statistical modelling. It will also provide a text for students meeting the ideas of statistical modelling for the first time.

Directions in Robust Statistics and Diagnostics
Directions in Robust Statistics and Diagnostics

Author: Werner Stahel

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 384

ISBN-13: 1461244447

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This IMA Volume in Mathematics and its Applications DIRECTIONS IN ROBUST STATISTICS AND DIAGNOSTICS is based on the proceedings of the first four weeks of the six week IMA 1989 summer program "Robustness, Diagnostics, Computing and Graphics in Statistics". An important objective of the organizers was to draw a broad set of statisticians working in robustness or diagnostics into collaboration on the challenging problems in these areas, particularly on the interface between them. We thank the organizers of the robustness and diagnostics program Noel Cressie, Thomas P. Hettmansperger, Peter J. Huber, R. Douglas Martin, and especially Werner Stahel and Sanford Weisberg who edited the proceedings. A vner Friedman Willard Miller, Jr. PREFACE Central themes of all statistics are estimation, prediction, and making decisions under uncertainty. A standard approach to these goals is through parametric mod elling. Parametric models can give a problem sufficient structure to allow standard, well understood paradigms to be applied to make the required inferences. If, how ever, the parametric model is not completely correct, then the standard inferential methods may not give reasonable answers. In the last quarter century, particularly with the advent of readily available computing, more attention has been paid to the problem of inference when the parametric model used is not correctly specified.

Scientific Software Systems
Scientific Software Systems

Author: J. C. Mason

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 268

ISBN-13: 9400908415

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The main aim of this book is to present a broader view of scientific software than has been common in the past. The provision of scientific software is no longer a matter of just writing 'good computer programs', but rather it is concerned with the development of an integrated software system wI-,ich offers the user facilities which approach all that he needs in terms of speed, accuracy and convenience. This means that due account must, for example, be taken of the high-speed computing capabilities of parallel processors, the exact computing features of symbolic mathematical systems, the presentational potentialities of computer graphics, and the advisory aspects of knowledge-based and expert systems. When suites of numerical software programs or routines are supported by such ranges of facilities, then they can be justly described as 'scientific software systems', and that is why we have adopted such a title here. The assembly of this book was a direct consequence of the running of a one-day international symposium, with the same broad aim of advocating a 'systems approach', under the title 'Scientific Software and Systems'. This Symposium was held at the Royal Military College of Science (RMCS) in Shrivenham on July 11, 1988 and was attended by 85 people. A very busy but most enjoyable day included invited talks, poster presentations and demonstrations of software products, not to mention various social activi ties.

Statistical Modelling
Statistical Modelling

Author: Adriano Decarli

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 352

ISBN-13: 1461236800

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This volume constitutes the Proceedings of the joint meeting of GLIM89 and the 4th International Workshop on statistical Modelling, held in Trento, Italy, from 17 to 21 July 1989. The meeting aimed to bring together researchers interested in the development and application of generalized linear modelling in GLIM and those interested in statistical modelling in its widest sense. This joint meeting built upon the success of previous workshops held in Innsbruck, perugia and Vienna, and upon the two previous GLIM conferences , GLIM82 and GLIM85. The Proceedings of the latter two being available as numbers 14 and 32 in the springer Verlag series of Lecture Notes in Statistics). Much statistical modelling is carried out using GLIM, as is apparent from many of the papers in these Proceedings; however, the Programme Committee were also keen on encouraging papers which discussed more general modelling techniques. Thus about a third of the papers in this volume are outside the GLIM framework. The Programme Committee specifically requested non-theoretical papers in addition to considering theoretical contributions. Thus there are papers in a wide range of practical areas, such as radio spectral occupancy, comparison of birthweights, intervals between births, accidents of railway workers, genetics, demography, medical trials, the social sciences and insurance. A wide range of theoretical developments are discussed, for example, overdispersion, non-exponential family modelling, novel approaches to analysing contingency tables, random effects models, Kalman Filtering, model checking and extensions of Wedderburn's theoretical underpinning of GLMs.

Methods And Models In Statistics: In Honour Of Professor John Nelder, Frs
Methods And Models In Statistics: In Honour Of Professor John Nelder, Frs

Author: David J Hand

Publisher: World Scientific

Published: 2004-07-06

Total Pages: 261

ISBN-13: 1783260696

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John Nelder was one of the most influential statisticians of his generation, having made an impact on many parts of the discipline. This book contains reviews of some of those areas, written by top researchers. It is accessible to non-specialists, and is noteworthy for its breadth of coverage.

Predictive Analytics For Business Using R
Predictive Analytics For Business Using R

Author: Russell R Barton

Publisher: World Scientific

Published: 2024-07-16

Total Pages: 464

ISBN-13: 9811293791

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The fields of mathematical statistics, statistical graphics, computer science and operations research have created the rich set of methods now called Analytics. Often analytics is characterized along three poles: descriptive analytics (what do data tell us), predictive analytics (what can be forecast based on the data, and with what certainty), and prescriptive analytics (how can the data inform changes to improve system performance).This book focuses on the second pole, predictive analytics. The areas of predicting a number, a class, and dynamic behavior are distinct, with different methods. This text has three parts based on these areas. Topics in predicting a number include simple and multiple linear regression, transformation of variables, analysis of observational data via cross-validation, the generalized linear model, designed experiments, and Gaussian process and neural network regression. Classification methods include neural networks, logistic regression, k-nearest neighbor, and linear discriminant analysis. Methods for predicting dynamic behavior include trend analysis, time series analysis and discrete-event dynamic simulation.Characterizing prediction uncertainty is a key focus of this text. The text provides analytic methods appropriate to each area, with an explicit process for applying such methods. Case data with corresponding R code are used to illustrate each method.Predictive Analytics for Business using R is designed for a hybrid class structure. Class sessions can be a blend of lecture format and flipped classroom case analyses. In a two-meetings-per-week fifteen-week structure, one day per week would be devoted to explaining methodology and presenting a case study, with the second day focused on coaching. Given the case structure, the text does not contain homework problems. Instead, at the end of each chapter there are links to cases posted online.