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Asymptotic Likelihood-Based Prediction Functions
Econometrica, 1990 This paper develops asymptotic prediction functions that approximate the shape of the density of future observations and correct for parameter uncertainty. The functions are based on extensions to a definition of predictive likelihood originally suggested by S. L. Lauritzen (1974) and D. Hinkley (1979).
Cooley, Thomas F, Parke, William R
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Model Selection for Discrete Dependent Variables: Better Statistics for Better Steaks
Journal of Agricultural and Resource Economics, 2004 Little research has been conducted on evaluating out-of-sample forecasts of discrete dependent variables. This study describes the large and small sample properties of two forecast evaluation techniques for discrete dependent variables: receiver-operator
F. Bailey Norwood +2 more
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Testable likelihoods for beyond-the-standard model fits
European Physical Journal C: Particles and Fields, 2023 Studying potential BSM effects at the precision frontier requires accurate transfer of information from low-energy measurements to high-energy BSM models.
Anja Beck, Méril Reboud, Danny van Dyk
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Penalized Composite Likelihood Estimation for Spatial Generalized Linear Mixed Models [PDF]
Journal of Sciences, Islamic Republic of IranWhen discussing non-Gaussian spatially correlated variables, generalized linear mixed models have enough flexibility for modeling various data types. However, the maximum likelihood methods are plagued with substantial calculations for large data sets ...
Mohsen Mohammadzadeh, Leyla Salehi
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New developments in the ROOT fitting classes [PDF]
EPJ Web of Conferences, 2019 The ROOT Mathematical and Statistical libraries have been recently improved both to increase their performance and to facilitate the modelling of parametric functions that can be used for performing maximum likelihood fits to data sets to estimate ...
Valls Xavier +3 more
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The Analysis of Ranking Data Using Score Functions and Penalized Likelihood
Austrian Journal of Statistics, 2017 In this paper, we consider different score functions in order summarize certain characteristics for one and two sample ranking data sets. Our approach is flexible and is based on embedding the nonparametric problem in a parametric framework.
Mayer Alvo, Hang Xu
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Full Open Population Capture-Recapture Models with Individual Covariates [PDF]
, 2010 Traditional analyses of capture-recapture data are based on likelihood functions that explicitly integrate out all missing data. We use a complete data likelihood (CDL) to show how a wide range of capture-recapture models can be easily fitted using ...
Barker, Richard J. +1 more
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Copula cosmology: Constructing a likelihood function [PDF]
Physical Review D, 2011 To estimate cosmological parameters from a given dataset, we need to construct a likelihood function, which sometimes has a complicated functional form. We introduce the copula, a mathematical tool to construct an arbitrary multivariate distribution function from one-dimensional marginal distribution functions with any given dependence structure. It is
Sato, Masanori +2 more
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Mathematics, 2022 This paper explores statistical inferences when the lifetime of product follows the inverse Nakagami distribution using progressive Type-II censored data.
Liang Wang +2 more
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Indoor Ultra-Wide Band Network Adjustment using Maximum Likelihood Estimation [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 This study is the part of our ongoing research on using ultra-wide band (UWB) technology for navigation at the Ohio State University. Our tests have indicated that the UWB two-way time-of-flight ranges under indoor circumstances follow a Gaussian mixture
Z. Koppanyi, C. K. Toth
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