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Approximated Information Analysis in Bayesian Inference
Entropy, 2015 In models with nuisance parameters, Bayesian procedures based on Markov Chain Monte Carlo (MCMC) methods have been developed to approximate the posterior distribution of the parameter of interest.
Jung In Seo, Yongku Kim
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On Birnbaum-Saunders Inference [PDF]
, 2008 The Birnbaum-Saunders distribution, also known as the fatigue-life distribution, is frequently used in reliability studies. We obtain adjustments to the Birnbaum--Saunders profile likelihood function. The modified versions of the likelihood function were
Araujo Jr, Carlos A. G. +2 more
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Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions
Econometrics, 2021 In this study, we investigate the estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression.
Jau-er Chen +2 more
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The Use of Chemical Sensors to Monitor Odour Emissions at Municipal Waste Biogas Plants
Applied Sciences, 2021 Municipal waste treatment plants are an important element of the urban area infrastructure, but also, they are a potential source of odour nuisance. Odour impact from municipal waste processing plants raises social concerns regarding the well-being of ...
Marta Wiśniewska +2 more
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Parameter uncertainties in weighted unbinned maximum likelihood fits
European Physical Journal C: Particles and Fields, 2022 Parameter estimation via unbinned maximum likelihood fits is central for many analyses performed in high energy physics. Unbinned maximum likelihood fits using event weights, for example to statistically subtract background contributions via the sPlot ...
Christoph Langenbruch
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Moderate deviations of minimum contrast estimators under contamination [PDF]
, 2003 Since statistical models are simplifications of reality, it is important in estimation theory to study the behavior of estimators also under distributions (slightly) different from the proposed model.
Inglot, Tadeusz +1 more
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On the Elimination of Nuisance Parameters [PDF]
Journal of the American Statistical Association, 1977 Abstract Eliminating nuisance parameters from a model is universally recognized as a major problem of statistics. A surprisingly large number of elimination methods have been proposed by various writers on the topic. In this article we propose to critically review two such elimination methods.
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Journal of Probability and Statistics, 2020 This paper provides an estimation method for an unknown parameter by extending weighted least-squared and pivot-based methods to the Gompertz distribution with the shape and scale parameters under the progressive Type-II censoring scheme, which induces a
Kyeongjun Lee, Jung-In Seo
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Linear Mixed-Effect Models Through the Lens of Hardy–Weinberg Disequilibrium
Frontiers in Genetics, 2022 For genetic association studies with related individuals, the linear mixed-effect model is the most commonly used method. In this report, we show that contrary to the popular belief, this standard method can be sensitive to departure from Hardy–Weinberg ...
Lin Zhang , Lei Sun , Lei Sun
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Estimating Relative Abundance From Count Data
Austrian Journal of Statistics, 2016 Much of the available information on large-scale patterns of animal abundance is based on count surveys. The data provided by such surveys are often influenced by nuisance factors affecting the numbers of animals counted, but unrelated to population size.
William A. Link, John R. Sauer
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