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Published **1971**
by Alabama U.P .

Written in English

**Edition Notes**

Published for Auburn University.

Statement | [by] L. Eiseley [et al]. |

Series | Franklin lectures in the sciences and humanities. 2nd series |

Contributions | Auburn University. |

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

Pagination | 87p. |

Number of Pages | 87 |

ID Numbers | |

Open Library | OL13657703M |

The Shape of likelihood: relevance and the university [by] Loren Eiseley [and others] Pref. by Taylor Littleton Published for Auburn University by the University of Alabama Press University Australian/Harvard Citation. Eiseley, Loren C. & Auburn University. Shape of likelihood. University, Published for Auburn University by the University of Alabama Press [] (OCoLC) Document Type: Book: All Authors / . Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. Computer Physics Communications 20 () North-Holland Publishing Company INTERPRETATION OF THE SHAPE OF THE LIKELIHOOD FUNCTION AROUND ITS MINIMUM F. JAMES Data Handling Division, CERN, Geneva, Switzerland It often happens that the solution of a minimum problem is itself straightforward, but the calculation or interpretation of the resulting parameter uncertainties, as determined by the shape .

The book addresses the use of likelihood in a number of familiar applications (parameter estimation, etc). The examples are numerous and clear. I find more recent writings to be more directly applicable, though. The real value of this book, for me, is the historical perspective that the Reviews: Figure xxx illustrates the normal likelihood for a representative sample of size n= Notice that the likelihood has the same bell-shape of a bivariate normal density Suppose σ 2 = The score and the log-likelihood ratio for the proﬁle like-lihood To ease notation, let us suppose that 0 and 0 are the true parameters in the distribution. We now consider the log-likelihood ratio 2 ⇢ max, L n(,)max L n(0,), () where 0 is the true parameter. However, to derive the limiting distribution in . In statistics, the likelihood function (often simply called the likelihood) measures the goodness of fit of a statistical model to a sample of data for given values of the unknown is formed from the joint probability distribution of the sample, but viewed and used as a function of the parameters only, thus treating the random variables as fixed at the observed values.

Shape of isocontours What do the isocontours of a multivariate Gaussian look like? As before, let’s consider the case where n = 2, and Σ is diagonal, i.e., x = x1 x2 µ = µ1 µ2 Σ = σ2 1 0 0 σ2 2 As we showed in the last section, p(x;µ,Σ) = 1 2πσ1σ2 exp − 1 2σ2 1 (x1 −µ1) 2 − 1 2σ2 2 (x2 −µ2) 2. (4). Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. library (bbmle) N function (shape, rate) { R = suppressWarnings (dgamma (xx, shape=shape, rate=rate)) return (-sum (log (R))) } Estimate the two parameters by using the mle2 function in the bbmle package as follows. This book presents the role of likelihood in a whole range of statistical problems, from a simple comparison of two accident rates to complex studies requiring generalized linear or semiparametric modeling. The book emphasizes that the likelihood is not simply a device to produce an estimate, but more importantly it is a tool for modeling/5(2).

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