Results 21 to 30 of about 7,257 (284)
Nonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models [PDF]
Journal of Business & Economic Statistics, 2021 Many panel data have the latent subgroup effect on individuals, and it is important to correctly identify these groups since the efficiency of resulting estimators can be improved significantly by pooling the information of individuals within each group.
Xiaoyu Zhang +3 more
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Mathematics, 2023 The presence of nonignorable missing response variables often leads to complex conditional distribution patterns that cannot be effectively captured through mean regression.
Jingxuan Guo +7 more
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A nonparametric approach for quantile regression [PDF]
Journal of Statistical Distributions and Applications, 2018 Quantile regression estimates conditional quantiles and has wide applications in the real world. Estimating high conditional quantiles is an important problem. The regular quantile regression (QR) method often designs a linear or non-linear model, then estimates the coefficients to obtain the estimated conditional quantiles.
Huang, Mei Ling, Nguyen, Christine
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Mathematics, 2023 Space-time varying coefficient models, which are used to identify the effects of covariates that change over time and spatial location, have been widely studied in recent years. One such model, called the quantile regression model, is particularly useful
Bertho Tantular +3 more
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Statistics in Medicine, 2023 We focus on identifying genomics risk factors of higher body mass index (BMI) incorporating a priori information, such as biological pathways. However, the commonly used methods to incorporate prior information provide a model for the mean function of ...
Peitao Wu, J. Dupuis, Ching‐Ti Liu
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Nonparametric Quantile Regression for Time Series with Replicated Observations and Its Application to Climate Data [PDF]
Statistical Science, 2021 This paper proposes a model-free nonparametric estimator of conditional quantile of a time series regression model where the covariate vector is repeated many times for different values of the response. This type of data is abound in climate studies.
S. Deb, Kaushik Jana
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Nonparametric and Semiparametric Quantile Regression via a New MM Algorithm
Journal of Computational And Graphical Statistics, 2023 Quantile regression is a popular method with a wide range of scientific applications, but the computation for quantile regression is challenging.
Bo Kai, Mian Huang, W. Yao, Yuexiao Dong
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Fuzzy Semi-Parametric Logistic Quantile Regression Model
Wasit Journal for Pure Sciences, 2023 In this paper, the fuzzy semi-parametric logistic quantile regression model was studied in the absence of special conditions in the classical regression models.
Ahmed Razzaq, Ayad H. shemaila
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Quantile-dependent expressivity of serum C-reactive protein concentrations in family sets [PDF]
PeerJ, 2021 Background “Quantile-dependent expressivity” occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g., C-reactive protein, CRP) is high or low relative to its distribution. We have previously shown that the heritabilities
Paul T. Williams
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qgam: Bayesian Nonparametric Quantile Regression Modeling in R [PDF]
Journal of Statistical Software, 2020 Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package.
M. Fasiolo +4 more
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