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Author: John Schmidt Ramberg Publisher: ISBN: Category : Mathematical statistics Languages : en Pages : 230
Book Description
Some 'indifference zone' multiple-decision selection procedure formulations of prediction problems involving multivariate normal populations are considered. These problems are of two types. Part I considers problems involving k bivariate normal populations, where the goal is to select the 'best' population. In this part the 'goodness' of the prediction is measured in terms of three different parameters -- the population conditional variance, the population correlation coefficient, and the absolute value of the population correlation coefficient. Part II considers the problem of selecting the best set of a preassigned number t variates from a set of k predictor variates for predicting a designated variate, the predictand. The 'best' set of predictor variates is defined to be the set of t variates for which the predictand has the smallest population conditional variance (or equivalently the largest population multiple correlation coefficient). Sample size requirements are obtained using asymptotic distribution theory of the transformed statistics.
Author: John Schmidt Ramberg Publisher: ISBN: Category : Mathematical statistics Languages : en Pages : 230
Book Description
Some 'indifference zone' multiple-decision selection procedure formulations of prediction problems involving multivariate normal populations are considered. These problems are of two types. Part I considers problems involving k bivariate normal populations, where the goal is to select the 'best' population. In this part the 'goodness' of the prediction is measured in terms of three different parameters -- the population conditional variance, the population correlation coefficient, and the absolute value of the population correlation coefficient. Part II considers the problem of selecting the best set of a preassigned number t variates from a set of k predictor variates for predicting a designated variate, the predictand. The 'best' set of predictor variates is defined to be the set of t variates for which the predictand has the smallest population conditional variance (or equivalently the largest population multiple correlation coefficient). Sample size requirements are obtained using asymptotic distribution theory of the transformed statistics.
Author: Agency for Health Care Research and Quality (U.S.) Publisher: Government Printing Office ISBN: 1587634236 Category : Medical Languages : en Pages : 236
Book Description
This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)
Author: Alan Miller Publisher: CRC Press ISBN: 1420035932 Category : Mathematics Languages : en Pages : 258
Book Description
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author ha
Author: Shanti S. Gupta Publisher: Academic Press ISBN: 1483260623 Category : Mathematics Languages : en Pages : 398
Book Description
Statistical Decision Theory and Related Topics is a collection of the papers presented at the Symposium on Statistical Decision Theory and Related Topics which was held on November 23-25, 1970 at Purdue University. The conference brought together research workers in decision theory and related topics. This volume contains twenty papers presented during the symposium and includes works on molecular studies of evolution, globally optimal procedure for one-sided comparisons, multiple decision theory, outlier detection, empirical Bayes slippage tests, and non-optimality of likelihood ratio tests for sequential detection of signals in Gaussian noise. Mathematicians and statisticians will find the book highly insightful.
Author: Publisher: ISBN: Category : Aeronautics Languages : en Pages : 764
Book Description
Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Author: Rob J Hyndman Publisher: OTexts ISBN: 0987507117 Category : Business & Economics Languages : en Pages : 380
Book Description
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Author: Max Kuhn Publisher: CRC Press ISBN: 1351609467 Category : Business & Economics Languages : en Pages : 266
Book Description
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Author: Peter Bruce Publisher: "O'Reilly Media, Inc." ISBN: 1491952911 Category : Computers Languages : en Pages : 322
Book Description
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Author: John Schmidt Ramberg
Author: John Schmidt Ramberg
Author: Agency for Health Care Research and Quality (U.S.)
Author: Alan Miller
Author: Shanti S. Gupta
Author: 
Author: Shanti S. Gupta
Author: Rob J Hyndman
Author: 
Author: Max Kuhn
Author: Peter Bruce