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| Author | : Kaihong Jiang |
| Publisher | : |
| Total Pages | : 230 |
| Release | : 1992 |
| Genre | : |
| ISBN | : |
| Author | : Burdick |
| Publisher | : CRC Press |
| Total Pages | : 238 |
| Release | : 1992-02-28 |
| Genre | : Mathematics |
| ISBN | : 9780824786441 |
Summarizes information scattered in the technical literature on a subject too new to be included in most textbooks, but which is of interest to statisticians, and those who use statistics in science and education, at an advanced undergraduate or higher level. Overviews recent research on constructin
| Author | : Gutti Jogesh Babu |
| Publisher | : CRC Press |
| Total Pages | : 242 |
| Release | : 1996-08-01 |
| Genre | : Mathematics |
| ISBN | : 9780412983917 |
Modern astronomers encounter a vast range of challenging statistical problems, yet few are familiar with the wealth of techniques developed by statisticians. Conversely, few statisticians deal with the compelling problems confronted in astronomy. Astrostatistics bridges this gap. Authored by a statistician-astronomer team, it provides professionals and advanced students in both fields with exposure to issues of mutual interest. In the first half of the book the authors introduce statisticians to stellar, galactic, and cosmological astronomy and discuss the complex character of astronomical data. For astronomers, they introduce the statistical principles of nonparametrics, multivariate analysis, time series analysis, density estimation, and resampling methods. The second half of the book is organized by statistical topic. Each chapter contains examples of problems encountered astronomical research and highlights methodological issues. The final chapter explores some controversial issues in astronomy that have a strong statistical component. The authors provide an extensive bibliography and references to software for implementing statistical methods. The "marriage" of astronomy and statistics is a natural one and benefits both disciplines. Astronomers need the tools and methods of statistics to interpret the vast amount of data they generate, and the issues related to astronomical data pose intriguing challenges for statisticians. Astrostatistics paves the way to improved statistical analysis of astronomical data and provides a common ground for future collaboration between the two fields.
| Author | : Achim Dörre |
| Publisher | : Springer |
| Total Pages | : 109 |
| Release | : 2019-05-13 |
| Genre | : Mathematics |
| ISBN | : 9811362416 |
This book introduces readers to statistical methodologies used to analyze doubly truncated data. The first book exclusively dedicated to the topic, it provides likelihood-based methods, Bayesian methods, non-parametric methods, and linear regression methods. These procedures can be used to effectively analyze continuous data, especially survival data arising in biostatistics and economics. Because truncation is a phenomenon that is often encountered in non-experimental studies, the methods presented here can be applied to many branches of science. The book provides R codes for most of the statistical methods, to help readers analyze their data. Given its scope, the book is ideally suited as a textbook for students of statistics, mathematics, econometrics, and other fields.
| Author | : Esa Uusipaikka |
| Publisher | : CRC Press |
| Total Pages | : 328 |
| Release | : 2008-07-25 |
| Genre | : Mathematics |
| ISBN | : 1420060384 |
A Cohesive Approach to Regression Models Confidence Intervals in Generalized Regression Models introduces a unified representation-the generalized regression model (GRM)-of various types of regression models. It also uses a likelihood-based approach for performing statistical inference from statistical evidence consisting of data a
| Author | : Kenneth Brian Fairbanks |
| Publisher | : |
| Total Pages | : 37 |
| Release | : 1979 |
| Genre | : Estimation theory |
| ISBN | : |
A sequential life test for the exponential location parameter was given by Epstein and Sobel (1955). This sequential test may be modified by truncating the test at r sub 0 failures and/or at total test time t'. There may be a need or a desire to also estimate the parameter after the test decision, using the test data. Bryant and Schmee (1979) have given confidence intervals for the mean lifetime, theta, from a truncated sequential test scheme, using methods which depend heavily on numerical techniques using a computer. A more flexible approach is considered using a martingale inequality which was also given by Wald (1947) in another context. Interval estimates are found which are functions of a positive constant d which must be chosen less than an upper bound which is itself a function of the number of failures observed. It is suggested that d be chosen as a function of the sample path (i.e., after the test is complete). The validity of the confidence coefficient comes into question if this posterior selection of d is employed. Simulation studies indicate that the resulting intervals are usually conservative.
| Author | : A. Colin Cameron |
| Publisher | : Cambridge University Press |
| Total Pages | : 597 |
| Release | : 2013-05-27 |
| Genre | : Business & Economics |
| ISBN | : 1107717795 |
Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book, now in its second edition, provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics and quantitative social sciences. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.
| Author | : Markus Neuhäuser |
| Publisher | : Oxford University Press |
| Total Pages | : 161 |
| Release | : 2024-08-30 |
| Genre | : Mathematics |
| ISBN | : 0198872992 |
We live in the era of big data. However, small data sets are still common for ethical, financial, or practical reasons. Small sample sizes can cause researchers to seek out the most powerful methods to analyse their data, but they may also be wary that some methodologies and assumptions may not be appropriate when samples are small. The book offers advice on the statistical analysis of small data sets for various designs and levels of measurement, helping researchers to analyse such data sets, but also to evaluate and interpret others' analyses. The book discusses the potential challenges associated with a small sample, as well as the ways in which these challenges can be mitigated. General topics with strong relevance to small sample sizes such as meta-analysis, sequential and adaptive designs, and multiple testing are introduced. While the focus is on hypothesis tests and confidence intervals, Bayesian analyses are also covered. Code written in the statistical software R is presented to carry out the proposed methods, many of which are not limited to use on small data sets, and the book also discusses approaches to computing the power or the necessary sample size, respectively.
| Author | : Michael Smithson |
| Publisher | : SAGE |
| Total Pages | : 104 |
| Release | : 2003 |
| Genre | : Mathematics |
| ISBN | : 9780761924999 |
Smithson first introduces the basis of the confidence interval framework and then provides the criteria for "best" confidence intervals, along with the trade-offs between confidence and precision. Next, using a reader-friendly style with lots of worked out examples from various disciplines, he covers such pertinent topics as: the transformation principle whereby a confidence interval for a parameter may be used to construct an interval for any monotonic transformation of that parameter; confidence intervals on distributions whose shape changes with the value of the parameter being estimated; and, the relationship between confidence interval and significance testing frameworks, particularly regarding power.
| Author | : Adrian Colin Cameron |
| Publisher | : Cambridge University Press |
| Total Pages | : 597 |
| Release | : 2013-05-27 |
| Genre | : Business & Economics |
| ISBN | : 1107014166 |
This book provides the most comprehensive and up-to-date account of regression methods to explain the frequency of events.
