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Relevant bibliographies by topics / Tweedie’s formula

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Academic literature on the topic 'Tweedie’s formula'

Author: Grafiati

Published: 1 July 2021

Last updated: 29 July 2025

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Journal articles on the topic "Tweedie’s formula"

1

Efron, Bradley. "Tweedie’s Formula and Selection Bias." Journal of the American Statistical Association 106, no. 496 (2011): 1602–14. http://dx.doi.org/10.1198/jasa.2011.tm11181.

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2

Wu, Bing-Qi, Yen-Jen Wang, Chang-Cheng Chang, Tzong-Yuan Juang, Yung-Hsueh Huang, and Ying-Chuan Hsu. "Clinical Efficacy of Cysteamine Application for Melasma: A Meta-Analysis." Journal of Clinical Medicine 13, no. 23 (2024): 7483. https://doi.org/10.3390/jcm13237483.

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Background: Melasma is a challenging, acquired hyperpigmentary disorder. The gold standard treatment is Kligman’s formulation, which contains hydroquinone, tretinoin, and dexamethasone, but its long-term use is limited by the risk of exogenous ochronosis. Cysteamine, a tyrosinase inhibitor, reduces melanocyte activity and melanin production, showing strong depigmenting effects in patients resistant to Kligman’s formulation. Nonetheless, clinical studies have yielded inconsistent efficacy results. This meta-analysis aimed to assess the efficacy of cysteamine in treating melasma and to identify

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3

Liu, Laura, Hyungsik Roger Moon, and Frank Schorfheide. "Forecasting With Dynamic Panel Data Models." Econometrica 88, no. 1 (2020): 171–201. http://dx.doi.org/10.3982/ecta14952.

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This paper considers the problem of forecasting a collection of short time series using cross‐sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross‐sectional information to transform the unit‐specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a nonparame

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4

Bonat, Wagner H., Bent Jørgensen, Célestin C. Kokonendji, John Hinde, and Clarice G. B. Demétrio. "Extended Poisson–Tweedie: Properties and regression models for count data." Statistical Modelling 18, no. 1 (2017): 24–49. http://dx.doi.org/10.1177/1471082x17715718.

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We propose a new class of discrete generalized linear models based on the class of Poisson–Tweedie factorial dispersion models with variance of the form [Formula: see text], where [Formula: see text] is the mean and [Formula: see text] and [Formula: see text] are the dispersion and Tweedie power parameters, respectively. The models are fitted by using an estimating function approach obtained by combining the quasi-score and Pearson estimating functions for the estimation of the regression and dispersion parameters, respectively. This provides a flexible and efficient regression methodology for

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5

Shi, Jianhong, Xiuqin Bai, and Weixing Song. "Tweedie-type formulae for a multivariate Laplace distribution." Statistics & Probability Letters 183 (April 2022): 109329. http://dx.doi.org/10.1016/j.spl.2021.109329.

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Shi, Jianhong, Jing Feng, and Weixing Song. "Estimation in Linear Regression with Laplace Measurement Error Using Tweedie-Type Formula." Journal of Systems Science and Complexity 32, no. 4 (2018): 1211–30. http://dx.doi.org/10.1007/s11424-018-7205-x.

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7

Taylor, Greg. "BAYESIAN CHAIN LADDER MODELS." ASTIN Bulletin 45, no. 1 (2014): 75–99. http://dx.doi.org/10.1017/asb.2014.25.

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AbstractThe literature on Bayesian chain ladder models is surveyed. Both Mack and cross-classified forms of the chain ladder are considered. Both cases are examined in the context of error terms distributed according to a member of the exponential dispersion family. Tweedie and over-dispersed Poisson errors follow as special cases. Bayesian cross-classified chain ladder models may randomise row, column or diagonal parameters. Column and diagonal randomisation has been largely absent from the literature until recently. The present paper allows randomisation of row and column parameters. The Bay

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8

Efron, Bradley. "Machine learning and the James–Stein estimator." Japanese Journal of Statistics and Data Science, June 30, 2023. http://dx.doi.org/10.1007/s42081-023-00209-y.

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AbstractIt is now 62 years since the publication of James and Stein’s seminal article on the estimation of a multivariate normal mean vector. The paper made a spectacular first impression on the statistical community through its demonstration of inadmissability of the maximum likelihood estimator. It continues to be influential, but not for the initial reasons. Empirical Bayes shrinkage estimation, now a major topic, found its early justification in the James–Stein formula. Less obvious downstream topics include Tweedie’s formula and Benjamini and Hochberg’s false discovery rate algorithm. Thi

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9

García Trillos, Nicolás, and Bodhisattva Sen. "A new perspective on denoising based on optimal transport." Information and Inference: A Journal of the IMA 13, no. 4 (2024). http://dx.doi.org/10.1093/imaiai/iaae029.

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Abstract In the standard formulation of the classical denoising problem, one is given a probabilistic model relating a latent variable $\varTheta \in \varOmega \subset{\mathbb{R}}^{m} \; (m\ge 1)$ and an observation $Z \in{\mathbb{R}}^{d}$ according to $Z \mid \varTheta \sim p(\cdot \mid \varTheta )$ and $\varTheta \sim G^{*}$, and the goal is to construct a map to recover the latent variable from the observation. The posterior mean, a natural candidate for estimating $\varTheta $ from $Z$, attains the minimum Bayes risk (under the squared error loss) but at the expense of over-shrinking the $

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Dissertations / Theses on the topic "Tweedie’s formula"

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Duan, Xiuwen. "Revisiting Empirical Bayes Methods and Applications to Special Types of Data." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42340.

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Empirical Bayes methods have been around for a long time and have a wide range of applications. These methods provide a way in which historical data can be aggregated to provide estimates of the posterior mean. This thesis revisits some of the empirical Bayesian methods and develops new applications. We first look at a linear empirical Bayes estimator and apply it on ranking and symbolic data. Next, we consider Tweedie’s formula and show how it can be applied to analyze a microarray dataset. The application of the formula is simplified with the Pearson system of distributions. Saddlepoin

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