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Relevant bibliographies by topics / Distributional regression

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Academic literature on the topic 'Distributional regression'

Author: Grafiati

Published: 11 December 2022

Last updated: 31 July 2025

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Journal articles on the topic "Distributional regression"

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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Distributional regression"

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Kasturiratna, Dhanuja. "Assessing the Distributional Assumptions in One-Way Regression Model." Bowling Green State University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1148479945.

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Hirvela, Kyle Ray. "Park Access and Distributional Inequities in Pinellas County, Florida." Scholar Commons, 2011. http://scholarcommons.usf.edu/etd/3150.

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Although environmental justice research has traditionally focused on environmental disamenities and health hazards, recent studies have begun to examine social inequities in the distribution of urban amenities such as street trees and parks that provide several direct and indirect health benefits to local residents. This thesis adds to this knowledge by evaluating distributional inequities in both distribution and access to parks in Pinellas County, the most densely populated and one of the most racially segregated counties in Florida. An important objective was to determine if neighborhoods w

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Pihl, Svante, and Leonardo Olivetti. "An Empirical Comparison of Static Count Panel Data Models: the Case of Vehicle Fires in Stockholm County." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412014.

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In this paper we study the occurrences of outdoor vehicle fires recorded by the Swedish Civil Contingencies Agency (MSB) for the period 1998-2019, and build static panel data models to predict future occurrences of fire in Stockholm County. Through comparing the performance of different models, we look at the effect of different distributional assumptions for the dependent variable on predictive performance. Our study concludes that treating the dependent variable as continuous does not hamper performance, with the exception of models meant to predict more uncommon occurrences of fire. Further

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Afrifa-Yamoah, Ebenezer. "Imputation, modelling and optimal sampling design for digital camera data in recreational fisheries monitoring." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2387.

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Digital camera monitoring has evolved as an active application-oriented scheme to help address questions in areas such as fisheries, ecology, computer vision, artificial intelligence, and criminology. In recreational fisheries research, digital camera monitoring has become a viable option for probability-based survey methods, and is also used for corroborative and validation purposes. In comparison to onsite surveys (e.g. boat ramp surveys), digital cameras provide a cost-effective method of monitoring boating activity and fishing effort, including night-time fishing activities. However, there

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Pic, Romain. "Statistical postprocessing of ensemble forecasts : theory, application in weather forecasting and verification." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD018.

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Cette thèse porte sur l'utilisation de méthodes de post-traitement statistiques dans le but d'améliorer les prévisions d'ensemble. Les prévisions d'ensemble sont des prévisions composées de différents membres dont la diversité tente de capturer l'incertitude liée à la prédiction. Les prévisions d'ensemble souffrent de biais et de sous-dispersion et un post-traitement est donc nécessaire afin d'améliorer leur performance. D'un point de vue théorique, cette thèse apporte des résultats sur le taux de convergence en régression distributionnelle en termes de continuous ranked probability score. De

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Lai, Pik-ying, and 黎碧瑩. "Lp regression under general error distributions." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30287844.

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Chen, Xinyu. "Inference in Constrained Linear Regression." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/405.

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Regression analyses constitutes an important part of the statistical inference and has great applications in many areas. In some applications, we strongly believe that the regression function changes monotonically with some or all of the predictor variables in a region of interest. Deriving analyses under such constraints will be an enormous task. In this work, the restricted prediction interval for the mean of the regression function is constructed when two predictors are present. I use a modified likelihood ratio test (LRT) to construct prediction intervals.

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Wei, Yan. "Robust mixture regression models using t-distribution." Kansas State University, 2012. http://hdl.handle.net/2097/14110.

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Master of Science<br>Department of Statistics<br>Weixin Yao<br>In this report, we propose a robust mixture of regression based on t-distribution by extending the mixture of t-distributions proposed by Peel and McLachlan (2000) to the regression setting. This new mixture of regression model is robust to outliers in y direction but not robust to the outliers with high leverage points. In order to combat this, we also propose a modified version of the proposed method, which fits the mixture of regression based on t-distribution to the data after adaptively trimming the high leverage points. We

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Parker, I. "Transformation in regression, estimation, testing and modelling." Thesis, University of St Andrews, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384598.

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Chang, Kai-Ming. "Bayesian regression and discrimination with many variables." Thesis, University College London (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268337.

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Books on the topic "Distributional regression"

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Chernozhukov, Victor. Inference for distributional effects using instrumental quantile regression. Massachusetts Institute of Technology, Dept. of Economics, 2002.

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Krishnamurthy, Chandra Kiran B. The distributional impacts of climate change on Indian agriculture: A quantile regression approach. Madras School of Economics, 2012.

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Menard, Scott W. Logistic regression. Sage Publications, 2009.

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Freeman, James M. Firm-size distributions: A piecewise regression analysis. University of Manchester Institute of Science and Technology, 1985.

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Györfi, László, Michael Kohler, Adam Krzyżak, and Harro Walk. A Distribution-Free Theory of Nonparametric Regression. Springer New York, 2002. http://dx.doi.org/10.1007/b97848.

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missing], [name. A distribution-free theory of nonparametric regression. Springer, 2003.

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Chernozhukov, Victor. Inference on counterfactual distributions. Massachusetts Institute of Technology, Dept. of Economics, 2008.

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Chernozhukov, Victor. Inference on counterfactual distributions. Massachusetts Institute of Technology, Dept. of Economics, 2008.

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Heckman, James J. Fifty years of mincer earnings regressions. National Bureau of Economic Research, 2003.

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Knight, Keith. Asympotitics for L1 regression estimators under general conditions. University of Toronto, Dept. of Statistics, 1997.

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Book chapters on the topic "Distributional regression"

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian D. Marx. "Distributional Regression Models." In Regression. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-63882-8_10.

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Silbersdorff, Alexander. "Estimating and Assessing Distributional Regression." In Analysing Inequalities in Germany. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65331-0_3.

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Marra, Giampiero, and Rosalba Radice. "Core concepts in copula regression." In Copula Additive Distributional Regression Using R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003593195-1.

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Dias, Sónia, and Paula Brito. "Regression Analysis with the Distribution and Symmetric Distribution Model." In Analysis of Distributional Data. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315370545-13.

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Marra, Giampiero, and Rosalba Radice. "Binary outcomes." In Copula Additive Distributional Regression Using R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003593195-5.

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Marra, Giampiero, and Rosalba Radice. "Binary and continuous outcomes." In Copula Additive Distributional Regression Using R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003593195-9.

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Marra, Giampiero, and Rosalba Radice. "Ordinal and continuous outcomes." In Copula Additive Distributional Regression Using R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003593195-8.

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Marra, Giampiero, and Rosalba Radice. "Count outcomes." In Copula Additive Distributional Regression Using R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003593195-3.

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Marra, Giampiero, and Rosalba Radice. "Ordinal outcomes." In Copula Additive Distributional Regression Using R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003593195-6.

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Marra, Giampiero, and Rosalba Radice. "Continuous outcomes." In Copula Additive Distributional Regression Using R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003593195-2.

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Conference papers on the topic "Distributional regression"

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Gultekin, Ece, Berkay Ozkan, and Evren Eyup Taskinoglu. "Machine Learning Based Stress Prediction Around the Hole of Attachment Lug Structures for Flaw Tolerance Evaluations." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-203.

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This study investigates the stress concentration and damage tolerance of lug structures, with an application example using the horizontal tail plane lug of a light utility helicopter. Using Finite Element Analysis (FEA), stress distributions around the lug hole were simulated under varying load conditions to understand how different loading angles and magnitudes affect stress concentrations. A machine learning approach was employed to predict stress distributions based on a dataset generated from FEA simulations. Several regression models were tested and Random Forest Regression model yields t

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Zhong, Yujun, Hao Huang, Si Zhang, et al. "Multi-Timescale Electrical Load Probabilistic Forecasting Method Based on Dual Attention Mechanism and Quantile Regression." In 2024 China International Conference on Electricity Distribution (CICED). IEEE, 2024. http://dx.doi.org/10.1109/ciced63421.2024.10754214.

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Schall, Baptiste, Rodolphe Anty, and Lionel Fillatre. "Optimal One-hot Logistic Regression for Tree-based Distribution Classification." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889704.

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Hu, Bowei, Bo Liu, Wenpeng Luan, Wenbin Liu, Ruiyao Jia, and Feng Wang. "Adaptive Abnormal Condition Detection for Low-Voltage Distribution Network based on the Quantile Regression Gradient Boosting Decision Tree." In 2024 China International Conference on Electricity Distribution (CICED). IEEE, 2024. http://dx.doi.org/10.1109/ciced63421.2024.10753795.

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Tajima, Yoshiyuki, and Tomoki Hamagami. "Ordinal Regression Based on the Distributional Distance Between Labels." In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2021. http://dx.doi.org/10.1109/smc52423.2021.9658911.

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Lang, Stefan, and Julian Granna. "Rental Price Dynamics in Germany: A Distributional Regression Model with Heterogenous Covariate Effects." In 29th Annual European Real Estate Society Conference. European Real Estate Society, 2023. http://dx.doi.org/10.15396/eres2023_130.

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Hassanpour, Negar, and Russell Greiner. "CounterFactual Regression with Importance Sampling Weights." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/815.

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Perhaps the most pressing concern of a patient diagnosed with cancer is her life expectancy under various treatment options. For a binary-treatment case, this translates into estimating the difference between the outcomes (e.g., survival time) of the two available treatment options – i.e., her Individual Treatment Effect (ITE). This is especially challenging to estimate from observational data, as that data has selection bias: the treatment assigned to a patient depends on that patient's attributes. In this work, we borrow ideas from domain adaptation to address the distributional shift betwee

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Jorge, Germano A. Z., and Thiago A. S. Pardo. "SteamBR: a dataset for game reviews and evaluation of a state-of-the-art method for helpfulness prediction." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/brasnam.2023.230132.

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The digital revolution has led to exponential growth in user-generated content, including ratings and reviews, across numerous online platforms. One such platform is Steam, a multifaceted digital distribution network primarily for video games, that also functions as an active social network. Like many e-commerce, travel, and restaurant platforms, Steam users rely heavily on reviews to inform their purchasing decisions. However, the vast amount of data and varying quality of reviews may hinder the utility of such reviews. Furthermore, there is a significant challenge in assessing the helpfulnes

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Xia, Haifeng, Pu Wang, Toshiaki Koike-Akino, Ye Wang, Philip Orlik, and Zhengming Ding. "Adversarial Bi-Regressor Network for Domain Adaptive Regression." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/501.

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Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi) localization, it is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective cross- domain regression model. Specifically, a discrepant bi-regressor architecture is developed to maximize the difference of bi-regressor to discover uncertain target instances far from the source distribution, and then an

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Wu, Dazhong, Connor Jennings, Janis Terpenny, Robert Gao, and Soundar Kumara. "Data-Driven Prognostics Using Random Forests: Prediction of Tool Wear." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2679.

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Manufacturers have faced an increasing need for the development of predictive models that help predict mechanical failures and remaining useful life of a manufacturing system or its system components. Model-based or physics-based prognostics develops mathematical models based on physical laws or probability distributions, while an in-depth physical understanding of system behaviors is required. In practice, however, some of the distributional assumptions do not hold true. To overcome the limitations of model-based prognostics, data-driven methods have been increasingly applied to machinery pro

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Reports on the topic "Distributional regression"

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Chetverikov, Denis, Bradley Larsen, and Christopher Palmer. IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21033.

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Mitchell, James, and Saeed Zaman. The distributional predictive content of measures of inflation expectations. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202331.

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This paper examines the predictive relationship between the distribution of realized inflation in the US and measures of inflation expectations from households, firms, financial markets, and professional forecasters. To allow for nonlinearities in the predictive relationship we use quantile regression methods. We find that the ability of households to predict future inflation, relative to that of professionals, firms, and the market, increases with inflation. While professional forecasters are more accurate in the middle of the inflation density, households’ expectations are more useful in the

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Mitchell, James, Aubrey Poon, and Dan Zhu. Constructing density forecasts from quantile regressions: multimodality in macro-financial dynamics. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202212r.

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Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the "data speak." Simulation evidence and an application revisiting GDP growth uncertainties in the US demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile regre

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Chernozhukov, Victor, Ivan Fernandez-Val, and Martin Weidner. Network and panel quantile effects via distribution regression. The IFS, 2018. http://dx.doi.org/10.1920/wp.cem.2018.2118.

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Weidner, Martin, Ivan Fernandez-Val, and Victor Chernozhukov. Network and Panel Quantile Effects Via Distribution Regression. The IFS, 2020. http://dx.doi.org/10.1920/wp.cem.2020.2720.

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Chernozhukov, Victor, Martin Weidner, and Ivan Fernandez-Val. Network and panel quantile effects via distribution regression. The IFS, 2018. http://dx.doi.org/10.1920/wp.cem.2018.7018.

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Chung, Steve, Jaymin Kwon, and Yushin Ahn. Forecasting Commercial Vehicle Miles Traveled (VMT) in Urban California Areas. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2315.

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This study investigates commercial truck vehicle miles traveled (VMT) across six diverse California counties from 2000 to 2020. The counties—Imperial, Los Angeles, Riverside, San Bernardino, San Diego, and San Francisco—represent a broad spectrum of California’s demographics, economies, and landscapes. Using a rich dataset spanning demographics, economics, and pollution variables, we aim to understand the factors influencing commercial VMT. We first visually represent the geographic distribution of the counties, highlighting their unique characteristics. Linear regression models, particularly

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Poterba, James, and Kim Rueben. The Distribution of Public Sector Wage Premia: New Evidence Using Quantile Regression Methods. National Bureau of Economic Research, 1994. http://dx.doi.org/10.3386/w4734.

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Pessino, Carola, Alejandro Rasteletti, Daniel Artana, and Nora Lustig. Distributional Effects of Taxation in Latin America. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005230.

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This chapter analyzes the incidence on income distribution by a comprehensive array of direct and indirect taxes in ten Latin American countries circa 2018. The study finds that although there is a significant heterogeneity, the redistributive impact is equalizing for direct taxes and unequalizing for indirect taxes. Overall, redistribution through taxes, without accounting for spending effects and interactions, is slightly equalizing for some countries and unequalizing for others, but the burden on the poor is high and even higher than on the rich. This is mainly a consequence of the high sha

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Fritsch, Nicholas. Tail Sensitivity of US Bank Net Interest Margins: A Bayesian Penalized Quantile Regression Approach. Federal Reserve Bank of Cleveland, 2025. https://doi.org/10.26509/frbc-wp-202509.

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Bank net interest margins (NIM) have been historically stable in the US on average, but this stability deteriorated in the post-2020 period, particularly in the tails of the distribution. Recent literature disagrees on the extent to which banks hedge interest rate risk, and past literature shows that credit risk and persistence are also important considerations for bank NIM. I use a novel approach to Bayesian dynamic panel quantile regression to document heterogeneity in US bank NIM estimated sensitivities to interest rates, credit risk, and own persistence. I find increased sensitivity to int

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