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Related papers: Semiparametric Discrete Choice Models for Bundles

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This paper proposes the asymmetric linear double autoregression, which jointly models the conditional mean and conditional heteroscedasticity characterized by asymmetric effects. A sufficient condition is established for the existence of a…

Methodology · Statistics 2021-04-22 Songhua Tan , Qianqian Zhu

Discriminative linear models are a popular tool in machine learning. These can be generally divided into two types: The first is linear classifiers, such as support vector machines, which are well studied and provide state-of-the-art…

Machine Learning · Computer Science 2012-07-02 Koby Crammer , Amir Globerson

Discrete data are abundant and often arise as counts or rounded data. These data commonly exhibit complex distributional features such as zero-inflation, over-/under-dispersion, boundedness, and heaping, which render many parametric models…

Methodology · Statistics 2023-02-27 Daniel R. Kowal , Bohan Wu

An inference procedure is proposed to provide consistent estimators of parameters in a modal regression model with a covariate prone to measurement error. A score-based diagnostic tool exploiting parametric bootstrap is developed to assess…

Methodology · Statistics 2024-07-02 Qingyang Liu , Xianzheng Huang

This paper investigates bootstrap-based bias correction of semiparametric estimators of the long memory parameter, $d$, in fractionally integrated processes. The re-sampling method involves the application of the sieve bootstrap to data…

Methodology · Statistics 2016-03-08 Don S. Poskitt , Gael M. Martin , Simone D. Grose

Multiple systems estimation using a Poisson loglinear model is a standard approach to quantifying hidden populations where data sources are based on lists of known cases. Information criteria are often used for selecting between the large…

Methodology · Statistics 2023-11-23 Bernard W. Silverman , Lax Chan , Kyle Vincent

Machine learning can provide predictions with disparate outcomes, in which subgroups of the population (e.g., defined by age, gender, or other sensitive attributes) are systematically disadvantaged. In order to comply with upcoming…

Machine Learning · Computer Science 2024-01-25 Moritz von Zahn , Oliver Hinz , Stefan Feuerriegel

This paper analyzes several different biases that emerge from the (possibly) low-precision nonparametric ingredient in a semiparametric model. We show that both the variance part and the bias part of the nonparametric ingredient can lead to…

Statistics Theory · Mathematics 2020-10-15 Jungjun Choi , Xiye Yang

This paper offers a new approach to address the model uncertainty in (potentially) divergent-dimensional single-index models (SIMs). We propose a model-averaging estimator based on cross-validation, which allows the dimension of covariates…

Methodology · Statistics 2022-06-14 Jiahui Zou , Wendun Wang , Xinyu Zhang , Guohua Zou

We study modeling and identification of processes with a spectral density matrix of low rank. Equivalently, we consider processes having an innovation of reduced dimension for which Prediction Error Methods (PEM) algorithms are not directly…

Systems and Control · Electrical Eng. & Systems 2021-05-11 Giorgio Picci , Wenqi Cao , Anders Lindquist

For modeling the serial dependence in time series of counts, various approaches have been proposed in the literature. In particular, models based on a recursive, autoregressive-type structure such as the well-known integer-valued…

Methodology · Statistics 2025-07-16 Maxime Faymonville , Carsten Jentsch

Bundling, the practice of jointly selling two or more products at a discount, is a widely used strategy in industry and a well examined concept in academia. Historically, the focus has been on theoretical studies in the context of…

Machine Learning · Computer Science 2020-02-04 Madhav Kumar , Dean Eckles , Sinan Aral

In this article, we introduce the concept of model confidence bounds (MCB) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confidence interval for parameter estimation, the MCB identifies…

Methodology · Statistics 2018-07-27 Yang Li , Yuetian Luo , Davide Ferrari , Xiaonan Hu , Yichen Qin

Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making. Logit-based models are dominant in applied work, largely due to their convenient closed-form…

Methodology · Statistics 2026-04-06 Easton Huch , Michael Keane

For linear models with a diverging number of parameters, it has recently been shown that modified versions of Bayesian information criterion (BIC) can identify the true model consistently. However, in many cases there is little…

Methodology · Statistics 2011-07-26 Heng Lian

Clustering is a widely deployed unsupervised learning tool. Model-based clustering is a flexible framework to tackle data heterogeneity when the clusters have different shapes. Likelihood-based inference for mixture distributions often…

Machine Learning · Statistics 2023-05-30 Yubo Zhuang , Xiaohui Chen , Yun Yang

We develop a novel procedure for constructing confidence bands for components of a sparse additive model. Our procedure is based on a new kernel-sieve hybrid estimator that combines two most popular nonparametric estimation methods in the…

Machine Learning · Statistics 2018-02-14 Junwei Lu , Mladen Kolar , Han Liu

We present new methods for multilabel classification, relying on ensemble learning on a collection of random output graphs imposed on the multilabel and a kernel-based structured output learner as the base classifier. For ensemble learning,…

Machine Learning · Computer Science 2013-11-19 Hongyu Su , Juho Rousu

Simultaneous variable selection and statistical inference is challenging in high-dimensional data analysis. Most existing post-selection inference methods require explicitly specified regression models, which are often linear, as well as…

Methodology · Statistics 2026-03-19 Shangyuan Ye , Shauna Rakshe , Ye Liang

Nonparametric maximum likelihood estimation is intended to infer the unknown density distribution while making as few assumptions as possible. To alleviate the over parameterization in nonparametric data fitting, smoothing assumptions are…

Machine Learning · Statistics 2021-04-21 YunPeng Li , ZhaoHui Ye