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Related papers: Semiparametric Discrete Choice Models for Bundles

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Variational methods are widely used for approximate posterior inference. However, their use is typically limited to families of distributions that enjoy particular conjugacy properties. To circumvent this limitation, we propose a family of…

Machine Learning · Computer Science 2012-06-22 Samuel Gershman , Matt Hoffman , David Blei

In this article, we develop a semiparametric Bayesian estimation and model selection approach for partially linear additive models in conditional quantile regression. The asymmetric Laplace distribution provides a mechanism for Bayesian…

Computation · Statistics 2013-07-11 Yuao Hu , Kaifeng Zhao , Heng Lian

We propose a framework for nonparametric identification and estimation of discrete choice models with unobserved choice sets. We recover the joint distribution of choice sets and preferences from a panel dataset on choices. We assume that…

Econometrics · Economics 2021-06-22 Victor H. Aguiar , Nail Kashaev

In this paper, we consider nonparametric multidimensional finite mixture models and we are interested in the semiparametric estimation of the population weights. Here, the i.i.d. observations are assumed to have at least three components…

Statistics Theory · Mathematics 2017-12-14 Elisabeth Gassiat , Judith Rousseau , Elodie Vernet

This paper addresses the problem of full model estimation for non-parametric finite mixture models. It presents an approach for selecting the number of components and the subset of discriminative variables (i.e., the subset of variables…

Statistics Theory · Mathematics 2021-12-13 Marie Du Roy de Chaumaray , Matthieu Marbac

Semiparametric single-index assumptions are convenient and widely used dimen\-sion reduction approaches that represent a compromise between the parametric and fully nonparametric models for regressions or conditional laws. In a mean…

Statistics Theory · Mathematics 2014-10-21 Samuel Maistre , Valentin Patilea

Among semiparametric regression models, partially linear additive models provide a useful tool to include additive nonparametric components as well as a parametric component, when explaining the relationship between the response and a set…

Methodology · Statistics 2024-02-01 Graciela Boente , Alejandra Martínez

This paper introduces a new type of probabilistic semiparametric model that takes advantage of data binning to reduce the computational cost of kernel density estimation in nonparametric distributions. Two new conditional probability…

Machine Learning · Computer Science 2026-04-02 Rafael Sojo , Javier Díaz-Rozo , Concha Bielza , Pedro Larrañaga

This article introduces an iterative distributed computing estimator for the multinomial logistic regression model with large choice sets. Compared to the maximum likelihood estimator, the proposed iterative distributed estimator achieves…

Econometrics · Economics 2024-12-03 Yanqin Fan , Yigit Okar , Xuetao Shi

The spectral bundle method proposed by Helmberg and Rendl is well established for solving large-scale semidefinite programs (SDP) thanks to its low per iteration computational complexity and strong practical performance. In this paper, we…

Optimization and Control · Mathematics 2022-11-08 Lijun Ding , Benjamin Grimmer

Existing identification and estimation methods for semiparametric sample selection models rely heavily on exclusion restrictions. However, it is difficult in practice to find a credible excluded variable that has a correlation with…

Econometrics · Economics 2024-12-03 Zhewen Pan , Yifan Zhang

We propose a new estimator for nonparametric binary choice models that does not impose a parametric structure on either the systematic function of covariates or the distribution of the error term. A key advantage of our approach is its…

Econometrics · Economics 2026-01-13 Guo Yan

The paper studies binary classification and aims at estimating the underlying regression function which is the conditional expectation of the class labels given the inputs. The regression function is the key component of the Bayes optimal…

Machine Learning · Statistics 2019-03-26 Balázs Csanád Csáji , Ambrus Tamás

Traditional variable selection methods could fail to be sign consistent when irrepresentable conditions are violated. This is especially critical in high-dimensional settings when the number of predictors exceeds the sample size. In this…

Methodology · Statistics 2022-04-26 Fei Xue , Annie Qu

Extremile regression, as a least squares analog of quantile regression, is potentially useful tool for modeling and understanding the extreme tails of a distribution. However, existing extremile regression methods, as nonparametric…

Methodology · Statistics 2025-07-03 Rong Jiang , Keming Yu , Jiangfeng Wang

Given data on the choices made by consumers for different offer sets, a key challenge is to develop parsimonious models that describe and predict consumer choice behavior while being amenable to prescriptive tasks such as pricing and…

Machine Learning · Statistics 2025-04-15 Yanqiu Ruan , Xiaobo Li , Karthyek Murthy , Karthik Natarajan

Structured additive distributional regression models offer a versatile framework for estimating complete conditional distributions by relating all parameters of a parametric distribution to covariates. Although these models efficiently…

Methodology · Statistics 2023-11-14 Jana Kleinemeier , Nadja Klein

Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the prediction of multiple neural networks by collecting votes from each network and computing the difference in those predictions. In this paper,…

Machine Learning · Computer Science 2023-07-10 Illia Oleksiienko , Alexandros Iosifidis

We design a debiased parametric bootstrap framework for statistical inference from differentially private data. Existing usage of the parametric bootstrap on privatized data ignored or avoided handling possible biases introduced by the…

Methodology · Statistics 2026-04-10 Zhanyu Wang , Arin Chang , Jordan Awan

In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference. We consider an ensemble-based scheme where each model/particle corresponds to a perturbation of…

Machine Learning · Computer Science 2020-06-09 Dimitrios Milios , Pietro Michiardi , Maurizio Filippone