Results 141 to 150 of about 91,138 (340)
Empirical-Likelihood-Based Inference for Partially Linear Models
MathematicsPartially linear models find extensive application in biometrics, econometrics, social sciences, and various other fields due to their versatility in accommodating both parametric and nonparametric elements.
Haiyan Su, Linlin Chen
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Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values [PDF]
, 2012 Hannes Leeb, Benedikt M. Pötscher
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A Bayes factor framework for unified parameter estimation and hypothesis testing
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract The Bayes factor, the data‐based updating factor of the prior to posterior odds of two hypotheses, is a natural measure of statistical evidence for one hypothesis over the other. We show how Bayes factors can also be used for parameter estimation.
Samuel Pawel
wiley +1 more source
Higher Order Estimating Equations for High-dimensional Models
, 2015 We introduce a new method of estimation of parameters in semiparametric and nonparametric models. The method is based on estimating equations that are $U$-statistics in the observations.
Li, Lingling +4 more
core
A Bayesian look at nuisance parameters
Trabajos de Estadistica Y de Investigacion Operativa, 1980 The elimination of nuisance parameters has classically been tackled by variousad hoc devices, and has led to a number of attempts to define partial sufficiency and ancillarity. The Bayesian approach is clearly defined. This paper examines some classical procedures in order to see when they can be given a Bayesian justification.
openaire +2 more sources
From tetrachoric to kappa: How to assess reliability on binary scales
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract Reliability is crucial in psychometrics, reflecting the extent to which a measurement instrument can discriminate between individuals or items. While classical test theory and intraclass correlation coefficients are well‐established for quantitative scales, estimating reliability for binary outcomes presents unique challenges due to their ...
Sophie Vanbelle
wiley +1 more source
To vary or not to vary: A flexible empirical Bayes factor for testing variance components
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract Random effects are the gold standard for capturing structural heterogeneity, such as individual differences or temporal dependence. Yet testing their presence is difficult because variance components are constrained to be non‐negative, creating a boundary problem. This paper introduces a flexible empirical Bayes factor (EBF) for testing random
Fabio Vieira, Hongwei Zhao, Joris Mulder
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GMM with Many Weak Moment Conditions and Nuisance Parameters: General Theory and Applications to Causal Inference [PDF]
Rui Wang, Kwun Chuen Gary Chan, Ting Ye
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Advancing the understanding of the human dimensions of Eurasian lynx reintroduction in Scotland
Conservation Science and Practice, EarlyView.To identify perceived impacts of a lynx reintroduction among stakeholders and to explore factors shaping these perceptions, we conducted 34 unstructured interviews with stakeholders within and in potentially suitable habitat for a lynx reintroduction and surrounding areas in Scotland.
Faye L. Whiley +4 more
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