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This study presents two new algorithms for solving linear stochastic bandit problems. The proposed methods use an approach from non-parametric statistics called bootstrapping to create confidence bounds. This is achieved without making any…

Machine Learning · Statistics 2016-05-05 Nandan Sudarsanam , Balaraman Ravindran

In a mixture of linear regression model, the regression coefficients are treated as random vectors that may follow either a continuous or discrete distribution. We propose two Expectation-Maximization (EM) algorithms to estimate this prior…

Methodology · Statistics 2025-10-17 Andrew Welbaum , Wanli Qiao

We propose to address the common problem of linear estimation in linear statistical models by using a model selection approach via penalization. Depending then on the framework in which the linear statistical model is considered namely the…

Statistics Theory · Mathematics 2009-09-11 Ikhlef Bechar

We present a new approach to semiparametric inference using corrected posterior distributions. The method allows us to leverage the adaptivity, regularization and predictive power of nonparametric Bayesian procedures to estimate…

Methodology · Statistics 2023-06-21 Andrew Yiu , Edwin Fong , Chris Holmes , Judith Rousseau

In genetic studies, not only can the number of predictors obtained from microarray measurements be extremely large, there can also be multiple response variables. Motivated by such a situation, we consider semiparametric dimension reduction…

Methodology · Statistics 2013-09-25 Heng Lian , Shujie Ma

Semi-supervised pose estimation is a practically challenging task for computer vision. Although numerous excellent semi-supervised classification methods have emerged, these methods typically use confidence to evaluate the quality of…

Computer Vision and Pattern Recognition · Computer Science 2023-11-06 Jiaqi Wu , Junbiao Pang , Qingming Huang

We consider semiparametric location-scatter models for which the $p$-variate observation is obtained as $X=\Lambda Z+\mu$, where $\mu$ is a $p$-vector, $\Lambda$ is a full-rank $p\times p$ matrix and the (unobserved) random $p$-vector $Z$…

Statistics Theory · Mathematics 2012-02-24 Pauliina Ilmonen , Davy Paindaveine

In this paper we propose a new method of estimation for discrete choice demand models when individual level data are available. The method employs a two-step procedure. Step 1 predicts the choice probabilities as functions of the observed…

Applications · Statistics 2020-10-19 Nick Doudchenko , Evgeni Drynkin

To address the difficult problem of multi-step ahead prediction of non-parametric autoregressions, we consider a forward bootstrap approach. Employing a local constant estimator, we can analyze a general type of non-parametric time series…

Methodology · Statistics 2023-11-02 Dimitris N. Politis , Kejin Wu

This study proposes a mixed logit model with multivariate nonparametric finite mixture distributions. The support of the distribution is specified as a high-dimensional grid over the coefficient space, with equal or unequal intervals…

Econometrics · Economics 2018-02-08 Akshay Vij , Rico Krueger

The latent class model is a powerful unsupervised clustering algorithm for categorical data. Many statistics exist to test the fit of the latent class model. However, traditional methods to evaluate those fit statistics are not always…

Methodology · Statistics 2018-01-30 Geert H. van Kollenburg , Joris Mulder , Jeroen K. Vermunt

This paper studies the performance of the spectral method in the estimation and uncertainty quantification of the unobserved preference scores of compared entities in a general and more realistic setup. Specifically, the comparison graph…

Methodology · Statistics 2024-03-04 Jianqing Fan , Zhipeng Lou , Weichen Wang , Mengxin Yu

We develop a new spatial semidiscrete multiscale method based upon the edge multiscale methods to solve semilinear parabolic problems with heterogeneous coefficients and smooth initial data. This method allows for a cheap spatial…

Numerical Analysis · Mathematics 2025-12-16 Leonardo A. Poveda , Shubin Fu , Guanglian Li , Eric Chung

Local variable selection aims to test for the effect of covariates on an outcome within specific regions. We outline a challenge that arises in the presence of non-linear effects and model misspecification. Specifically, for common…

Methodology · Statistics 2024-08-02 David Rossell , Arnold Kisuk Kseung , Ignacio Saez , Michele Guindani

Based on the tensor-based large margin distribution and the nonparallel support tensor machine, we establish a novel classifier for binary classification problem in this paper, termed the Large Margin Distribution based NonParallel Support…

Optimization and Control · Mathematics 2025-07-18 Zhuolin Du , Yisheng Song

Statistical model checking delivers quantitative verification results with statistical guarantees by applying Monte Carlo simulation to formal models. It scales to model sizes and model types that are out of reach for exhaustive, analytical…

Logic in Computer Science · Computer Science 2025-11-18 Pedro R. D'Argenio , Arnd Hartmanns , Patrick Wienhöft , Mark van Wijk

This paper studies estimation and inference of heterogeneous peer effects featuring group fixed effects and slope heterogeneity under latent structure. We adapt the Classifier-Lasso algorithm to consistently discover latent structures and…

Methodology · Statistics 2026-02-09 Zhongjian Lin , Zhentao Shi , Yapeng Zheng

Difference-in-Differences (DiD) and Synthetic Control (SC) are widely used methods for causal inference in panel data, each with distinct strengths and limitations. We propose a novel method for short-panel causal inference that integrates…

Econometrics · Economics 2025-09-26 Yixiao Sun , Haitian Xie , Yuhang Zhang

This paper considers inference for conditional moment inequality models using a multiscale statistic. We derive the asymptotic distribution of this test statistic and use the result to propose feasible critical values that have a simple…

Applications · Statistics 2015-12-10 Timothy B. Armstrong , Hock Peng Chan

This work develops formal statistical inference procedures for machine learning ensemble methods. Ensemble methods based on bootstrapping, such as bagging and random forests, have improved the predictive accuracy of individual trees, but…

Machine Learning · Statistics 2015-09-11 Lucas Mentch , Giles Hooker