statistical inference a short course

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Statistical Inference

Author : Michael J. Panik
ISBN : 9781118309803
Genre : Mathematics
File Size : 87. 16 MB
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A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causality. To ensure a thorough understanding of all key concepts, Statistical Inference provides numerous examples and solutions along with complete and precise answers to many fundamental questions, including: How do we determine that a given dataset is actually a random sample? With what level of precision and reliability can a population sample be estimated? How are probabilities determined and are they the same thing as odds? How can we predict the level of one variable from that of another? What is the strength of the relationship between two variables? The book is organized to present fundamental statistical concepts first, with later chapters exploring more advanced topics and additional statistical tests such as Distributional Hypotheses, Multinomial Chi-Square Statistics, and the Chi-Square Distribution. Each chapter includes appendices and exercises, allowing readers to test their comprehension of the presented material. Statistical Inference: A Short Course is an excellent book for courses on probability, mathematical statistics, and statistical inference at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and practitioners who would like to develop further insights into essential statistical tools.

All Of Statistics

Author : Larry Wasserman
ISBN : 9780387217369
Genre : Mathematics
File Size : 45. 71 MB
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Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con ducted in statistics departments while data mining and machine learning re search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo rithms are more scalable than statisticians ever thought possible. Formal sta tistical theory is more pervasive than computer scientists had realized.

Targeted Learning

Author : Mark J. van der Laan
ISBN : 1441997822
Genre : Mathematics
File Size : 71. 7 MB
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The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.

Essentials Of Statistical Inference

Author : G. A. Young
ISBN : 0521548667
Genre : Mathematics
File Size : 20. 22 MB
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This textbook presents the concepts and results underlying the Bayesian, frequentist, and Fisherian approaches to statistical inference, with particular emphasis on the contrasts between them. Aimed at advanced undergraduates and graduate students in mathematics and related disciplines, it covers basic mathematical theory as well as more advanced material, including such contemporary topics as Bayesian computation, higher-order likelihood theory, predictive inference, bootstrap methods, and conditional inference.

Nonparametric Statistical Inference

Author : Jean Dickinson Gibbons
ISBN : 9780824755225
Genre : Mathematics
File Size : 46. 81 MB
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Thoroughly revised and reorganized, the fourth edition presents in-depth coverage of the theory and methods of the most widely used nonparametric procedures in statistical analysis and offers example applications appropriate for all areas of the social, behavioral, and life sciences. The book presents new material on the quantiles, the calculation of exact and simulated power, multiple comparisons, additional goodness-of-fit tests, methods of analysis of count data, and modern computer applications using MINITAB, SAS, and STATXACT. It includes tabular guides for simplified applications of tests and finding P values and confidence interval estimates.

Christian And Humanist Foundations For Statistical Inference

Author : Andrew M. Hartley
ISBN : 9781556355493
Genre : Religion
File Size : 50. 91 MB
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The Philosophy of the Law Idea (PLI) analyzes the manner in which religious beliefs control scientific theorizing. Religious beliefs control philosophical overviews of reality. Overviews of reality, also called ontologies, try to discover and disclose the essential nature of reality. They are concerned with what kinds of things exist and with the connections between the various types of properties and laws in human experience. Among such overviews are the biblically consistent overview provided by the PLI and certain humanist mathematicist and subjectivist overviews. The science of statistical inference seeks to evaluate the credibility of scientific hypotheses given empirical data. This essay reviews various popular paradigms, or systems of theories, concerning the ways that credibility may be evaluated, and identifies some ways that these religiously controlled overviews of reality have, in turn, controlled statistical paradigms. In particular, one paradigm harmonizes with the PLI's overview; another, with the subjectivist overview; and two others, with the mathematicist overview.

All Of Nonparametric Statistics

Author : Larry Wasserman
ISBN : 0387306234
Genre : Mathematics
File Size : 77. 99 MB
Format : PDF
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This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.

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