2 edition of **Practical use of higher-order asymptotics for multiparameter exponential families** found in the catalog.

Practical use of higher-order asymptotics for multiparameter exponential families

Donald A. Pierce

- 126 Want to read
- 12 Currently reading

Published
**1991**
by Dept. of Statistics, Oregon State University in Corvallis, Ore
.

Written in English

- Exponential families (Statistics),
- Mathematical statistics -- Asymptotic theory.,
- Method of steepest descent (Numerical analysis)

**Edition Notes**

Includes bibliographical references (p. 33-35).

Statement | by Donald A. Pierce and Dawn Peters. |

Series | Technical report -- no. 146., Technical report (Oregon State University. Dept. of Statistics) -- 146. |

Contributions | Peters, Dawn., Oregon State University. Dept. of Statistics. |

The Physical Object | |
---|---|

Pagination | 35 p. : |

Number of Pages | 35 |

ID Numbers | |

Open Library | OL16098040M |

The term "neo-Fisherian" highlights a unified review of background material (statistical models, likelihood, data and model reduction, first-order asymptotics) and inference in the presence of nuisance parameters (including pseudo-likelihoods), a self-contained introduction is given to exponential families, exponential dispersion Authors: Luigi Pace, Alessandra Salvan. Journal of the Royal Statistical Society: Series A (Statistics in Society) Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Inference and Asymptotics (Chapman & Hall/CRC Monographs on Statistics and Applied Probability Book 52) - Kindle edition by Cox, D.R.. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Inference and Asymptotics (Chapman & Hall/CRC Monographs on Statistics and Applied Probability Book 52).Manufacturer: Routledge. In cond: Approximate Conditional Inference for Logistic and Loglinear Models. Description Arguments Generation Methods References See Also. Description. Class of objects returned when performing approximate conditional inference for logistic and loglinear models. Arguments.

function call that created the cond object. formula. the model formula. family. the variance function. Practical use of higher order asymptotics for multiparameter exponential families (with Discussion). J. R. Statist. Soc. B, 54, We enjoyed reading the interesting, thought-provok ing article by Geyer and Meeden. In our comments we will try to place their work in perspective rela tive to the original proposals for exact and random ized confidence intervals for the binomial parameter. We propose a fuzzy version of the original binomial randomized confidence interval, due to Stevens ().

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Title: Practical Use of Higher Order Asymptotics for Multiparameter Exponential Families Created Date: Z. Pierce D, Peters D. Practical use of higher order asymptotics for multiparameter exponential families.

Journal of the Royal Statistical Society, Series B (Methodological) – Pierce D, Peters D. Improving on exact tests by approximate conditioning. Vol. 54, No. 3, Published by: Wiley for the Royal Statistical Society.

Practical Use of Higher Order Asymptotics for Multiparameter Exponential Families. Practical Use of Higher Order Asymptotics for Multiparameter Exponential Families (pp. Pierce, D. and Peters, D.

Practical use of higher-order asymptotics for multiparameter exponential families. Roy. Statist. Soc. 54, Pierce, D.A. and Bellio, R. (in prep). Modern likelihood-frequentist inference.

(Basis for this talk) Skovgaard, I. An explicit large-deviation approximation to one- parameter tests. Pierce D.A. and Peters D.

Practical use of higher order asymptotics for multiparameter exponential families (with Discussion).Journal of the Royal Statistical Society B Google ScholarCited by: 4.

Pierce, D.A. and Peters, D. () Practical use of higher-order asymptotics for multiparameter exponential families (with discussion). Journal of the Royal Statistical Society B 54, to by: 1. Canonical regression models for exponential families. D.A.

Pierce, D. PetersPractical use of higher order asymptotics for multiparameter exponential families (with discussion) Journal of the Royal Statistical Society, Series B, 54 (), pp.

Cited by: 2. Asymptotics and the theory of inference. Reid Full-text: Open access. PDF File ( KB) Abstract Practical use of higher-order asymptotics for multiparameter exponential families (with discussion).

Roy. Statist. Soc. Ser. Recently developed asymptotics based on saddlepoint methods provide important practical methods for multiparameter exponential families, especially in generalized linear models.

The aim here is to. Pierce, D. and Peters, D. Practical use of higher-order asymptotics for multiparameter exponential families. Roy. Statist. Soc. 54, Pierce, D. and Bellio, R. Effects of the reference set on frequentist inferences. Biometr Pierce, D.A. and Bellio, R. (in prep). Modern likelihood-frequentist inference.

Recently developed asymptotics based on saddlepoint methods provide important practical methods for multiparameter exponential families, especially in generalized linear models.

The aim here is to Author: Nancy Reid. On the equivalence of prospective and retrospective likelihood methods in case-control studies Ana-Maria Staicu. Department of Statistics, North Carolina State University, Practical use of higher order asymptotics for multiparameter exponential families, Higher-order accurate methods for retrospective sampling problems, Cited by: Contribution to the discussion of and : Practical use of higher-order asymptotics for multiparameter exponential families By L.

Pace and A. Salvan Year: Author: L. Pace and A. Salvan. Higher-order asymptotics in nonlinear regression. In Proceedings of the 14th International Workshop on Statistical Modelling, Graz, July 19–23, (H.

Friedl, A. Berghold and G. Kauermann, eds.) – Discussion of "Practical use of higher order asymptotics for multiparameter exponential families".

Journal of the Royal Statistical Society B, (), Proposed solution for Problem College Mathematics Journal, (), (with D.A.S.

Fraser and N. Reid) Exponential linear model: a two-pass procedure for saddlepoint. Pierce and D. Peters. Practical use of higher-order asymptotics for multiparameter exponential families.

Journal of the Royal Statistical Society Series B, –, 21 T. Severini. Likelihood Methods in Statistics. Oxford University Press, Oxford, 20, 21, 26 I. Skovgaard. An explicit large-deviation approx. Applied Asymptotics: Case Studies in Small-Sample Statistics In fields such as biology, medical sciences, sociology and economics researchers often Constructed exponential families 8 Likelihood approximations the past three decades into a theory of higher order asymptotics.

While this theory leads. After a unified review of background material (statistical models, likelihood, data and model reduction, first-order asymptotics) and inference in the presence of nuisance parameters (including pseudo-likelihoods), a self-contained introduction is given to exponential families, exponential dispersion models, generalized linear models, and group.

Higher-order inference about a scalar parameter in the presence of nuisance parameters can be achieved by bootstrapping, in circumstances where the p We use cookies to enhance your experience on our continuing to use our website, you are agreeing to our use of by: Higher-order asymptotic theory is used to derive p -values that achieve superior accuracy compared to the p -values obtained from traditional tests for inference about parameters of the multinomial logit by: 2.

The book also describes inference in multivariate (multiparameter) models, exploring asymptotic normality and optimality of MLEs, Wald and Rao statistics, generalized linear models, and more.

Mathematical Statistics: Basic Ideas and Selected Topics, Volume II will be published in One Parameter Exponential Family Multiparameter Exponential Family Building Exponential Families.

Deﬁnition. Let X be a random variable/vector with sample space X⊂ R. q. and probability model P. θ. The class of probability models P = {P. θ,θ ∈ Θ} is a one-parameter exponential family if the density/pmf function p(x | θ) can be written:File Size: KB.Lecture UMPU tests in exponential families Neyman structure Let U(X) be a sufﬁcient statistic for P 2P and let P U be the family of distributions of U as P ranges over P.

A test is said to have Neyman structure w.r.t. U if E[T(X)jU] = a a.s. P U; Clearly, if T has Neyman structure, then E[T(X)] = EfE[T(X)jU]g= a P 2P ; i.e., T is similar.