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Journal articles on the topic 'Distributional regression'

Author: Grafiati

Published: 11 December 2022

Last updated: 31 July 2025

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1

Briseño Sanchez, Guillermo, Maike Hohberg, Andreas Groll, and Thomas Kneib. "Flexible instrumental variable distributional regression." Journal of the Royal Statistical Society: Series A (Statistics in Society) 183, no. 4 (2020): 1553–74. http://dx.doi.org/10.1111/rssa.12598.

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2

Stasinopoulos, Mikis D., Robert A. Rigby, and Fernanda De Bastiani. "GAMLSS: A distributional regression approach." Statistical Modelling 18, no. 3-4 (2018): 248–73. http://dx.doi.org/10.1177/1471082x18759144.

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Abstract: A tutorial of the generalized additive models for location, scale and shape (GAMLSS) is given here using two examples. GAMLSS is a general framework for performing regression analysis where not only the location (e.g., the mean) of the distribution but also the scale and shape of the distribution can be modelled by explanatory variables.

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3

Umlauf, Nikolaus, and Thomas Kneib. "A primer on Bayesian distributional regression." Statistical Modelling 18, no. 3-4 (2018): 219–47. http://dx.doi.org/10.1177/1471082x18759140.

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Abstract: Bayesian methods have become increasingly popular in the past two decades. With the constant rise of computational power, even very complex models can be estimated on virtually any modern computer. Moreover, interest has shifted from conditional mean models to probabilistic distributional models capturing location, scale, shape and other aspects of a response distribution, where covariate effects can have flexible forms, for example, linear, non-linear, spatial or random effects. This tutorial article discusses how to select models in the Bayesian distributional regression setting, h

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4

Alejo, Javier, Antonio Galvao, Julián Martínez-Iriarte, and Gabriel Montes-Rojas. "Generalized Recentered Influence Function Regressions." Econometrics 13, no. 2 (2025): 19. https://doi.org/10.3390/econometrics13020019.

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This paper suggests a generalization of covariate shifts to study distributional impacts on inequality and distributional measures. It builds on the recentered influence function (RIF) regression method, originally designed for location shifts in covariates, and extends it to general policy interventions, such as location–scale or asymmetric interventions. Numerical simulations for the Gini, Theil, and Atkinson indexes demonstrate strong performance across a myriad of cases and distributional measures. An empirical application examining changes in Mincerian equations is presented to illustrate

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McDonald, James B., and Jacob Triplett. "gintreg: Generalized interval regression." Stata Journal: Promoting communications on statistics and Stata 25, no. 1 (2025): 51–76. https://doi.org/10.1177/1536867x251322961.

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Many important research questions involve regression models in which the dependent variable is censored or reported in intervals rather than as a numerical value. A common approach to treating these problems is to assume that the data correspond to a certain distribution (for example, a normal distribution) and then apply maximum likelihood estimation. While this method is widely used in the literature, it can yield inconsistent estimators in the presence of either heteroskedasticity or distributional misspecification. The gintreg command is a partially adaptive maximum-likelihood estimation p

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Klein, Nadja, Thomas Kneib, Stephan Klasen, and Stefan Lang. "Bayesian structured additive distributional regression for multivariate responses." Journal of the Royal Statistical Society: Series C (Applied Statistics) 64, no. 4 (2014): 569–91. http://dx.doi.org/10.1111/rssc.12090.

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7

Silbersdorff, Alexander, Julia Lynch, Stephan Klasen, and Thomas Kneib. "Reconsidering the income-health relationship using distributional regression." Health Economics 27, no. 7 (2018): 1074–88. http://dx.doi.org/10.1002/hec.3656.

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8

Rios-Avila, Fernando. "Recentered influence functions (RIFs) in Stata: RIF regression and RIF decomposition." Stata Journal: Promoting communications on statistics and Stata 20, no. 1 (2020): 51–94. http://dx.doi.org/10.1177/1536867x20909690.

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Recentered influence functions (RIFs) are statistical tools popularized by Firpo, Fortin, and Lemieux (2009 , Econometrica 77: 953–973) for analyzing unconditional partial effects on quantiles in a regression analysis framework (unconditional quantile regressions). The flexibility and simplicity of these tools have opened the possibility to extend the analysis to other distributional statistics using linear regressions or decomposition approaches. In this article, I introduce one function and two commands to facilitate the use of RIFs in the analysis of outcome distributions: rifvar() is an eg

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9

Marshall, P., T. Szikszai, V. LeMay, and A. Kozak. "Testing the distributional assumptions of least squares linear regression." Forestry Chronicle 71, no. 2 (1995): 213–18. http://dx.doi.org/10.5558/tfc71213-2.

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The error terms in least squares linear regression are assumed to be normally distributed with equal variance (homoskedastic), and independent of one another. If any of these distributional assumptions are violated, several of the desirable properties of a least squares fit may not hold. A variety of statistical tests of the assumptions is available. The following are recommended for reasons of ease of use and discriminating power: the K2 test for testing for non-normality, either the Durbin-Watson test or the Q-test for testing for autocorrelation, and either Szroeter's or White's test for te

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10

Shen, Shu, and Xiaohan Zhang. "Distributional Tests for Regression Discontinuity: Theory and Empirical Examples." Review of Economics and Statistics 98, no. 4 (2016): 685–700. http://dx.doi.org/10.1162/rest_a_00595.

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11

Yang, Hojin, Veerabhadran Baladandayuthapani, Arvind U. K. Rao, and Jeffrey S. Morris. "Quantile Function on Scalar Regression Analysis for Distributional Data." Journal of the American Statistical Association 115, no. 529 (2019): 90–106. http://dx.doi.org/10.1080/01621459.2019.1609969.

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12

McDonald, James B., and Hieu Nguyen. "Heteroscedasticity and Distributional Assumptions in the Censored Regression Model." Communications in Statistics - Simulation and Computation 44, no. 8 (2014): 2151–68. http://dx.doi.org/10.1080/03610918.2013.851217.

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13

Uranchimeg, Sumiya, Hyun-Han Kwon, Byungsik Kim, and Tae-Woong Kim. "Changes in extreme rainfall and its implications for design rainfall using a Bayesian quantile regression approach." Hydrology Research 51, no. 4 (2020): 699–719. http://dx.doi.org/10.2166/nh.2020.003.

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Abstract This study aims to explore possible distributional changes in annual daily maximum rainfalls (ADMRs) over South Korea using a Bayesian multiple non-crossing quantile regression model. The distributional changes in the ADMRs are grouped into nine categories, focusing on changes in the location and scale parameters of the probability distribution. We identified seven categories for a distributional change in the selected stations. Most of the stations (28 of 50) are classified as Category III, which is characterized by an upward trend with an increase in variance in the distribution. Mo

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14

Silbersdorff, Alexander, and Kai Sebastian Schneider. "Distributional Regression Techniques in Socioeconomic Research on the Inequality of Health with an Application on the Relationship between Mental Health and Income." International Journal of Environmental Research and Public Health 16, no. 20 (2019): 4009. http://dx.doi.org/10.3390/ijerph16204009.

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This study addresses the much-discussed issue of the relationship between health and income. In particular, it focuses on the relation between mental health and household income by using generalized additive models of location, scale and shape and thus employing a distributional perspective. Furthermore, this study aims to give guidelines to applied researchers interested in taking a distributional perspective on health inequalities. In our analysis we use cross-sectional data of the German socioeconomic Panel (SOEP). We find that when not only looking at the expected mental health score of an

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15

Siniscalchi, Sabato Marco. "Vector-to-Vector Regression via Distributional Loss for Speech Enhancement." IEEE Signal Processing Letters 28 (2021): 254–58. http://dx.doi.org/10.1109/lsp.2021.3050386.

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16

Wiemann, Paul F. V., Nadja Klein, and Thomas Kneib. "Correcting for sample selection bias in Bayesian distributional regression models." Computational Statistics & Data Analysis 168 (April 2022): 107382. http://dx.doi.org/10.1016/j.csda.2021.107382.

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17

Moonga, Given, Stephan Böse-O’Reilly, Ursula Berger, et al. "Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach." PLOS ONE 16, no. 8 (2021): e0255073. http://dx.doi.org/10.1371/journal.pone.0255073.

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Background The burden of child under-nutrition still remains a global challenge, with greater severity being faced by low- and middle-income countries, despite the strategies in the Sustainable Development Goals (SDGs). Globally, malnutrition is the one of the most important risk factors associated with illness and death, affecting hundreds of millions of pregnant women and young children. Sub-Saharan Africa is one of the regions in the world struggling with the burden of chronic malnutrition. The 2018 Zambia Demographic and Health Survey (ZDHS) report estimated that 35% of the children under

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18

Reich, Brian J. "Spatiotemporal quantile regression for detecting distributional changes in environmental processes." Journal of the Royal Statistical Society: Series C (Applied Statistics) 61, no. 4 (2012): 535–53. http://dx.doi.org/10.1111/j.1467-9876.2011.01025.x.

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19

Schlosser, Lisa, Torsten Hothorn, Reto Stauffer, and Achim Zeileis. "Distributional regression forests for probabilistic precipitation forecasting in complex terrain." Annals of Applied Statistics 13, no. 3 (2019): 1564–89. http://dx.doi.org/10.1214/19-aoas1247.

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20

Ramsey, James B. "Regression over Timescale Decompositions: A Sampling Analysis of Distributional Properties." Economic Systems Research 11, no. 2 (1999): 163–84. http://dx.doi.org/10.1080/09535319900000012.

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21

Ozonur, Deniz, Hatice Tul Kubra Akdur, and Hulya Bayrak. "Comparisons of tests of distributional assumption in Poisson regression model." Communications in Statistics - Simulation and Computation 46, no. 8 (2016): 6197–207. http://dx.doi.org/10.1080/03610918.2016.1202267.

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22

TAJIMA, Yoshiyuki, and Tomoki HAMAGAMI. "Ordinal Regression Based on the Distributional Distance for Tabular Data." IEICE Transactions on Information and Systems E106.D, no. 3 (2023): 357–64. http://dx.doi.org/10.1587/transinf.2022edp7071.

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23

Linton, Oliver. "Edgeworth Approximation for MINPIN Estimators in Semiparametric Regression Models." Econometric Theory 12, no. 1 (1996): 30–60. http://dx.doi.org/10.1017/s0266466600006435.

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We examine the higher order asymptotic properties of semiparametric regression estimators that were obtained by the general MINPIN method described in Andrews (1989, Semiparametric Econometric Models: I. Estimation, Discussion paper 908, Cowles Foundation). We derive an order n−1 stochastic expansion and give a theorem justifying order n−1 distributional approximation of the Edgeworth type.

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24

Jackson, John E. "A Seemingly Unrelated Regression Model for Analyzing Multiparty Elections." Political Analysis 10, no. 1 (2002): 49–65. http://dx.doi.org/10.1093/pan/10.1.49.

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This paper develops an estimator for models of election returns in multiparty elections. It shares the same functional formas the Katz—King estimator but is computationally simpler, can be used with any number of parties, and is based on more conventional distributional assumptions. Small sample properties of the estimator are derived, which makes it particularly useful in many of the applications where there are a relatively small number of voting districts. The distributional assumptions are contained in two elements. The first treats the observed votes as the outcomes resulting from samplin

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25

Li, Wentao, Qingyun Duan, and Quan J. Wang. "Factors Influencing the Performance of Regression-Based Statistical Postprocessing Models for Short-Term Precipitation Forecasts." Weather and Forecasting 34, no. 6 (2019): 2067–84. http://dx.doi.org/10.1175/waf-d-19-0121.1.

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Abstract Statistical postprocessing models can be used to correct bias and dispersion errors in raw precipitation forecasts from numerical weather prediction models. In this study, we conducted experiments to investigate four factors that influence the performance of regression-based postprocessing models with normalization transformations for short-term precipitation forecasts. The factors are 1) normalization transformations, 2) incorporation of ensemble spread as a predictor in the model, 3) objective function for parameter inference, and 4) two postprocessing schemes, including distributio

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26

Chen, Jau-er, and Chen-Wei Hsiang. "Causal Random Forests Model Using Instrumental Variable Quantile Regression." Econometrics 7, no. 4 (2019): 49. http://dx.doi.org/10.3390/econometrics7040049.

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We propose an econometric procedure based mainly on the generalized random forests method. Not only does this process estimate the quantile treatment effect nonparametrically, but our procedure yields a measure of variable importance in terms of heterogeneity among control variables. We also apply the proposed procedure to reinvestigate the distributional effect of 401(k) participation on net financial assets, and the quantile earnings effect of participating in a job training program.

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27

Das, Debraj, and S. N. Lahiri. "Distributional consistency of the lasso by perturbation bootstrap." Biometrika 106, no. 4 (2019): 957–64. http://dx.doi.org/10.1093/biomet/asz029.

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Summary The lasso is a popular estimation procedure in multiple linear regression. We develop and establish the validity of a perturbation bootstrap method for approximating the distribution of the lasso estimator in a heteroscedastic linear regression model. We allow the underlying covariates to be either random or nonrandom, and show that the proposed bootstrap method works irrespective of the nature of the covariates. We also investigate finite-sample properties of the proposed bootstrap method in a moderately large simulation study.

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28

Lang, Moritz N., Georg J. Mayr, Reto Stauffer, and Achim Zeileis. "Bivariate Gaussian models for wind vectors in a distributional regression framework." Advances in Statistical Climatology, Meteorology and Oceanography 5, no. 2 (2019): 115–32. http://dx.doi.org/10.5194/ascmo-5-115-2019.

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Abstract. A new probabilistic post-processing method for wind vectors is presented in a distributional regression framework employing the bivariate Gaussian distribution. In contrast to previous studies, all parameters of the distribution are simultaneously modeled, namely the location and scale parameters for both wind components and also the correlation coefficient between them employing flexible regression splines. To capture a possible mismatch between the predicted and observed wind direction, ensemble forecasts of both wind components are included using flexible two-dimensional smooth fu

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29

Hohberg, Maike, Peter Pütz, and Thomas Kneib. "Treatment effects beyond the mean using distributional regression: Methods and guidance." PLOS ONE 15, no. 2 (2020): e0226514. http://dx.doi.org/10.1371/journal.pone.0226514.

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30

Michaelis, Patrick, Nadja Klein, and Thomas Kneib. "Bayesian Multivariate Distributional Regression With Skewed Responses and Skewed Random Effects." Journal of Computational and Graphical Statistics 27, no. 3 (2018): 602–11. http://dx.doi.org/10.1080/10618600.2017.1395343.

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31

Klein, Nadja, and Thomas Kneib. "Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression." Bayesian Analysis 11, no. 4 (2016): 1071–106. http://dx.doi.org/10.1214/15-ba983.

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32

Pötscher, Benedikt M., and Ulrike Schneider. "Distributional results for thresholding estimators in high-dimensional Gaussian regression models." Electronic Journal of Statistics 5 (2011): 1876–934. http://dx.doi.org/10.1214/11-ejs659.

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33

Li, Wenjuan, Wenying Wang, Jingsi Chen, and Weidong Rao. "Aggregate Kernel Inverse Regression Estimation." Mathematics 11, no. 12 (2023): 2682. http://dx.doi.org/10.3390/math11122682.

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Sufficient dimension reduction (SDR) is a useful tool for nonparametric regression with high-dimensional predictors. Many existing SDR methods rely on some assumptions about the distribution of predictors. Wang et al. proposed an aggregate dimension reduction method to reduce the dependence on the distributional assumptions. Motivated by their work, we propose a novel and effective method by combining the aggregate method and the kernel inverse regression estimation. The proposed approach can accurately estimate the dimension reduction directions and substantially improve the exhaustivity of t

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34

Kneib, Thomas, Nadja Klein, Stefan Lang, and Nikolaus Umlauf. "Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions." TEST 28, no. 1 (2019): 1–39. http://dx.doi.org/10.1007/s11749-019-00631-z.

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35

Smith, Thomas J., David A. Walker, and Cornelius M. McKenna. "An Exploration of Link Functions Used in Ordinal Regression." Journal of Modern Applied Statistical Methods 18, no. 1 (2020): 2–15. http://dx.doi.org/10.22237/jmasm/1556669640.

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The purpose of this study is to examine issues involved with choice of a link function in generalized linear models with ordinal outcomes, including distributional appropriateness, link specificity, and palindromic invariance are discussed and an exemplar analysis provided using the Pew Research Center 25th anniversary of the Web Omnibus Survey data. Simulated data are used to compare the relative palindromic invariance of four distinct indices of determination/discrimination, including a newly proposed index by Smith et al. (2017).

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36

Komárek, Arnošt, and Emmanuel Lesaffre. "The regression analysis of correlated interval-censored data." Statistical Modelling 9, no. 4 (2009): 299–319. http://dx.doi.org/10.1177/1471082x0900900403.

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The accelerated failure time (AFT) model is a useful alternative to the proportional hazard model for modelling interval-censored survival times. We illustrate the usefulness of a class of flexible AFT models. Flexibility is achieved by assuming that the distributional parts consist of penalized Gaussian mixtures. The AFT models are introduced and exemplified via research questions originating from a longitudinal dental study conducted in Flanders (North of Belgium). Emphasis is put on the analyzes which are performed using routines written in the R-language. They show the practical usefulness

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37

Nothdurft, Arne, Andreas Tockner, Sarah Witzmann, et al. "Spatial Prediction of Diameter Distributions for the Alpine Protection Forests in Ebensee, Austria, Using ALS/PLS and Spatial Distributional Regression Models." Remote Sensing 16, no. 12 (2024): 2181. http://dx.doi.org/10.3390/rs16122181.

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A novel Bayesian spatial distributional regression model is presented to predict forest structural diversity in terms of the distributions of the stem diameter at breast height (DBH) in the protection forests in Ebensee, Austria. The distributional regression approach overcomes the limitations and uncertainties of traditional regression modeling, in which the conditional mean of the response is regressed against explanatory variables. The distributional regression addresses the complete conditional response distribution, instead. In total 36,338 sample trees were measured via a handheld mobile

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38

Sánchez, Luis, Germán Ibacache-Pulgar, Carolina Marchant, and Marco Riquelme. "Modeling Environmental Pollution Using Varying-Coefficients Quantile Regression Models under Log-Symmetric Distributions." Axioms 12, no. 10 (2023): 976. http://dx.doi.org/10.3390/axioms12100976.

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Many phenomena can be described by random variables that follow asymmetrical distributions. In the context of regression, when the response variable Y follows such a distribution, it is preferable to estimate the response variable for predictor values using the conditional median. Quantile regression models can be employed for this purpose. However, traditional models do not incorporate a distributional assumption for the response variable. To introduce a distributional assumption while preserving model flexibility, we propose new varying-coefficients quantile regression models based on the fa

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39

Santos, Glauber Eduardo de Oliveira. "An efficient method for modelling tourists’ length of stay." Tourism Economics 22, no. 6 (2016): 1367–79. http://dx.doi.org/10.5367/te.2015.0490.

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Modelling tourists’ length of stay imposes relevant challenges to researchers. The log-linear ordinary least squares (OLS) regression is a simple but limited statistical technique due to its log-normal distributional assumption. Duration models allow more flexible distributional assumptions, but they create unnecessary statistical complexity. Considering these limitations, a third and most efficient alternative that joins statistical simplicity and distributional flexibility is examined in this article. Generalized linear models (GLMs) are used to explain tourists’ length of stay at each of 10

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40

Bjorndal, KA, AB Bolten, and M. Chaloupka. "Green turtle somatic growth dynamics: distributional regression reveals effects of differential emigration." Marine Ecology Progress Series 616 (May 9, 2019): 185–95. http://dx.doi.org/10.3354/meps12946.

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41

Whittaker, Tiffany A., Rachel T. Fouladi, and Natasha J. Williams. "Determining Predictor Importance In Multiple Regression Under Varied Correlational And Distributional Conditions." Journal of Modern Applied Statistical Methods 1, no. 2 (2002): 354–66. http://dx.doi.org/10.22237/jmasm/1036110360.

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42

Alzaid, A., and M. Al-Osh. "First-Order Integer-Valued Autoregressive (INAR (1)) Process: Distributional and Regression Properties." Statistica Neerlandica 42, no. 1 (1988): 53–61. http://dx.doi.org/10.1111/j.1467-9574.1988.tb01521.x.

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43

Alvarez, Maximiliano. "Distributional effects of environmental taxation: An approximation with a meta-regression analysis." Economic Analysis and Policy 62 (June 2019): 382–401. http://dx.doi.org/10.1016/j.eap.2018.10.003.

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44

Gayawan, Ezra, Oluwatoyin Deborah Fasusi, and Dipankar Bandyopadhyay. "Structured additive distributional zero augmented beta regression modeling of mortality in Nigeria." Spatial Statistics 35 (March 2020): 100415. http://dx.doi.org/10.1016/j.spasta.2020.100415.

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45

Månsson, Kristofer, Ghazi Shukur, and B. M. Golam Kibria. "A Simulation Study of Some Ridge Regression Estimators under Different Distributional Assumptions." Communications in Statistics - Simulation and Computation 39, no. 8 (2010): 1639–70. http://dx.doi.org/10.1080/03610918.2010.508862.

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46

Shiau, Jenq-Tzong, and Wen-Hong Huang. "Detecting distributional changes of annual rainfall indices in Taiwan using quantile regression." Journal of Hydro-environment Research 9, no. 3 (2015): 368–80. http://dx.doi.org/10.1016/j.jher.2014.07.006.

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47

Gupta, Vivek, Rahul Wadbude, Nagarajan Natarajan, Harish Karnick, Prateek Jain, and Piyush Rai. "Distributional Semantics Meets Multi-Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3747–54. http://dx.doi.org/10.1609/aaai.v33i01.33013747.

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We present a label embedding based approach to large-scale multi-label learning, drawing inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings. Besides leading to a highly scalable model for multi-label learning, our approach highlights interesting connections between label embedding methods commonly used for multi-label learning and paragraph embedding methods commonly used for learning representations of text data. The framework easily extends to incorporating auxiliary information such as

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48

Schnell, Patrick. "Comments on: Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions." TEST 28, no. 1 (2019): 46–51. http://dx.doi.org/10.1007/s11749-019-00632-y.

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49

Goicoa, T. "Comments on: Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions." TEST 28, no. 1 (2019): 40–42. http://dx.doi.org/10.1007/s11749-019-00633-x.

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50

Stasinopoulos, M. D., R. A. Rigby, G. Z. Heller, and F. De Bastiani. "Comments on: Modular regression—a Lego system for building structured additive distributional regression models with tensor product interactions." TEST 28, no. 1 (2019): 52–54. http://dx.doi.org/10.1007/s11749-019-00634-w.

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