Statistical inference based on a new weighted likelihood approach
نویسندگان
چکیده
منابع مشابه
Statistical Inference: An Integrated Bayesian/Likelihood Approach
To assess the strength of evidence from the data for competing parameter values or competing models, it uses likelihood ratios between the parameter values or the models. To interpret the likelihood ratios it uses a Bayesian approach which requires in general only the non-informative priors that are widely used in posterior inference about model parameters, though it can accommodate informative...
متن کاملWeighted Regret-Based Likelihood: A New Approach to Describing Uncertainty
Recently, Halpern and Leung [2012] suggested representing uncertainty by a weighted set of probability measures, and suggested a way of making decisions based on this representation of uncertainty: maximizing weighted regret. Their paper does not answer an apparently simpler question: what it means, according to this representation of uncertainty, for an event E to be more likely than an event ...
متن کاملWeighted empirical likelihood inference
A weighted empirical likelihood approach is proposed to take account of the heteroscedastic structure of the data. The resulting weighted empirical likelihood ratio statistic is shown to have a limiting chisquare distribution. A limited simulation study shows that the associated con*dence intervals for a population mean or a regression coe+cient have more accurate coverage probabilities and mor...
متن کاملComments on “A foundational justification for a weighted likelihood approach to inference”, by
Non-additive probability goes back to the very beginning of probability theory— the work of Jacob Bernoulli. Bernoulli’s calculus for combining arguments allowed both sides of a question to attain only small or zero probability, and he also thought the probabilities for two sides might sometimes add to more than one (Shafer 1978). Twentieth-century non-additive probability has roots in both mat...
متن کاملa weighted pairwise likelihood approach to multivariate ar(1) models
in this paper, the use of weighted pairwise likelihood instead of the full likelihood in estimating the parameters of the multivariate ar(1) is investigated. a closed formula for typical elements of the godambe information(sandwich information) is presented. some efficiency calculations are also given to discuss the feasibility andcomputational advantages of the weighted pairwise likelihood app...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Metrika
سال: 2020
ISSN: 0026-1335,1435-926X
DOI: 10.1007/s00184-020-00778-y