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by Thomas A. Severini
Download Likelihood Methods in Statistics (Oxford Statistical Science Series) fb2
Mathematics
  • Author:
    Thomas A. Severini
  • ISBN:
    0198506503
  • ISBN13:
    978-0198506508
  • Genre:
  • Publisher:
    Oxford University Press; 1 edition (January 18, 2001)
  • Pages:
    392 pages
  • Subcategory:
    Mathematics
  • Language:
  • FB2 format
    1795 kb
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    1773 kb
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    1667 kb
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    4.8
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Series: Oxford Statistical Science Series (Book 22). Hardcover: 392 pages.

Series: Oxford Statistical Science Series (Book 22).

Oxford Statistical Science Series. This book provides an introduction to the modern theory of likelihood-based statistical inference. This theory is characterized by several important features. One is the recognition that it is desirable to condition on relevant ancillary statistics. Another is that probability approximations are based on saddlepoint and closely related approximations that generally have very high accuracy.

Likelihoodist statistics or likelihoodism is an approach to statistics that exclusively or primarily uses the likelihood function. Likelihoodist statistics is a more minor school than the main approaches of Bayesian statistics and frequentist statistics, but has some adherents and applications. The central idea of likelihoodism is the likelihood principle: data are interpreted as evidence, and the strength of the evidence is measured by the likelihood function.

Cambridge University Data Science Society. Community organisation. Indian Statistical Service-ISS. Government organisation. Center for Statistics and Machine Learning, Princeton University. College & University. Cambridge University Department of Chemical Engineering and Biotechnology. Department of Computer Science, University of Oxford. Oxford Students, University of Oxford.

Statistical learning arose as a subfield of Statistics. In statistics, linear regression is a method to predict a target variable by fitting the best linear relationship between the dependent and independent variable

Statistical learning arose as a subfield of Statistics. Machine learning has a greater emphasis on large scale applications and prediction accuracy. In statistics, linear regression is a method to predict a target variable by fitting the best linear relationship between the dependent and independent variable. The best fit is done by making sure that the sum of all the distances between the shape and the actual observations at each point is as small as possible.

The MSc in Statistical Science is a twelve-month full-time taught master’s degree running from October to September each academic year. The MSc has a particular focus on modern nsive theory and methods. The MSc in Statistical Science will aim to train you to solve real-world statistical problems. When completing the course you should be able to choose an appropriate statistical method to solve a given problem of data analysis, implement the analysis on a computer and communicate your results clearly and succinctly.

Thomas A. Severini is a professor of statistics at Northwestern University. He is a fellow of the American Statistical Association and the author of Likelihood Methods in Statistics and Elements of Distribution Theory. He received his PhD in statistics from the University of Chicago. Learn about new offers and get more deals by joining our newsletter.

The book demonstates that much of modern statistics can be seen as special cases of categorical data models; both .

The book demonstates that much of modern statistics can be seen as special cases of categorical data models; both generalized linear models and proportional hazards models can be fitted as log linear models. More specialized topics such as Markov chains, overdispersion and random effects, are also covered. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical sciences.

By Thomas A. Severini. Likelihood Methods in Statistics (Oxford Statistical Science Series). Likelihood Methods in Statistics (Oxford Statistical Science Series) Close. 1 2 3 4 5. Want to Read. Internet Archive Wishlist.

Statistics offers a wide range of methods with which to analyze quality and performance data. It is based on the application of statistical test of hypothesis, and the resulting fuzzy sets are interpretable as fuzzy classes. Hypothesis testing and tests of statistical significance are one method of determining if any change is occurring in quality and performance within a healthcare organization (HCO). The method enables the construction of normal fuzzy sets, which can be adapted to have pseudo-triangular or pseudo-trapezoidal shape, both coherently with the corresponding probability distributions, by tuning the method parameters.

This book provides an introduction to the modern theory of likelihood-based statistical inference. This theory is characterized by several important features. One is the recognition that it is desirable to condition on relevant ancillary statistics. Another is that probability approximations are based on saddlepoint and closely related approximations that generally have very high accuracy. A third aspect is that, for models with nuisance parameters, inference is often based on marginal or conditional likelihoods, or approximations to these likelihoods. These methods have been shown often to yield substantial improvements over classical methods. The book also provide an up-to-date account of recent results in the field, which has been undergoing rapid development.