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While mixture of linear regressions (MLR) is a well-studied topic, prior works usually do not analyze such models for prediction error. In fact, {\em prediction} and {\em loss} are not well-defined in the context of mixtures. In this paper,…

Machine Learning · Statistics 2022-05-27 Avishek Ghosh , Arya Mazumdar , Soumyabrata Pal , Rajat Sen

This paper presents a backfitting-type method for estimating and forecasting a periodically correlated partially linear model with exogeneous variables and heteroskedastic input noise. A rate of convergence of the estimator is given. The…

Statistics Theory · Mathematics 2011-02-23 Xavier Brossat , Georges Oppenheim , Marie-Claude Viano

It is commonly accepted that some phenomena are social: for example, individuals' smoking habits often correlate with those of their peers. Such correlations can have a variety of explanations, such as direct contagion or shared…

Statistics Theory · Mathematics 2025-08-08 William W. Wang , Ali Jadbabaie

We are interested in learning data-driven representations that can generalize well, even when trained on inherently biased data. In particular, we face the case where some attributes (bias) of the data, if learned by the model, can severely…

Computer Vision and Pattern Recognition · Computer Science 2020-03-17 Ruggero Ragonesi , Riccardo Volpi , Jacopo Cavazza , Vittorio Murino

Segmented regression models offer model flexibility and interpretability as compared to the global parametric and the nonparametric models, and yet are challenging in both estimation and inference. We consider a four-regime segmented model…

Methodology · Statistics 2024-10-08 Han Yan , Song Xi Chen

This paper considers identification and estimation of distributional effect parameters that depend on the joint distribution of an outcome and another variable of interest ("treatment") in a setting with "two-sided" measurement error --…

Econometrics · Economics 2025-09-23 Brantly Callaway , Tong Li , Irina Murtazashvili , Emmanuel Tsyawo

We show that moment inequalities in a wide variety of economic applications have a particular linear conditional structure. We use this structure to construct uniformly valid confidence sets that remain computationally tractable even in…

Econometrics · Economics 2022-12-20 Isaiah Andrews , Jonathan Roth , Ariel Pakes

In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-series analysis: the analysis of observed data---typically univariate---via dynamical systems theory. Based on the concept of state-space…

Chaotic Dynamics · Physics 2015-06-24 Elizabeth Bradley , Holger Kantz

In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can be computationally expensive or even intractable if the…

A principled approach to understand network structures is to formulate generative models. Given a collection of models, however, an outstanding key task is to determine which one provides a more accurate description of the network at hand,…

Machine Learning · Statistics 2018-06-29 Toni Vallès-Català , Tiago P. Peixoto , Roger Guimerà , Marta Sales-Pardo

We consider the problem of learning a realization of a partially observed dynamical system with linear state transitions and bilinear observations. Under very mild assumptions on the process and measurement noises, we provide a finite time…

Machine Learning · Computer Science 2024-09-26 Yahya Sattar , Yassir Jedra , Sarah Dean

We propose new ensemble models for multivariate functional data classification as combinations of semi-metric-based weak learners. Our models extend current semi-metric-type methods from the univariate to the multivariate case, propose new…

Linear time-invariant systems are very popular models in system theory and applications. A fundamental problem in system identification that remains rather unaddressed in extant literature is to leverage commonalities amongst related linear…

Machine Learning · Statistics 2024-01-03 Aditya Modi , Mohamad Kazem Shirani Faradonbeh , Ambuj Tewari , George Michailidis

The identification of a linear system model from data has wide applications in control theory. The existing work that provides finite sample guarantees for linear system identification typically uses data from a single long system…

Machine Learning · Statistics 2025-05-09 Lei Xin , Baike She , Qi Dou , George Chiu , Shreyas Sundaram

In structured prediction problems where we have indirect supervision of the output, maximum marginal likelihood faces two computational obstacles: non-convexity of the objective and intractability of even a single gradient computation. In…

Machine Learning · Statistics 2016-08-11 Aditi Raghunathan , Roy Frostig , John Duchi , Percy Liang

Linear fusion of estimates under the condition of no knowledge of correlation of estimation errors has reached maturity. On the other hand, various cases of partial knowledge are still active research areas. A frequent motivation is to deal…

Signal Processing · Electrical Eng. & Systems 2025-06-16 Jiří Ajgl , Ondřej Straka

This paper considers the maximum likelihood estimation of panel data models with interactive effects. Motivated by applications in economics and other social sciences, a notable feature of the model is that the explanatory variables are…

Statistics Theory · Mathematics 2014-02-27 Jushan Bai , Kunpeng Li

Learning models that are robust to distribution shifts is a key concern in the context of their real-life applicability. Invariant Risk Minimization (IRM) is a popular framework that aims to learn robust models from multiple environments.…

Machine Learning · Computer Science 2023-04-04 Moulik Choraria , Ibtihal Ferwana , Ankur Mani , Lav R. Varshney

In many longitudinal settings, economic theory does not guide practitioners on the type of restrictions that must be imposed to solve the rotational indeterminacy of factor-augmented linear models. We study this problem and offer several…

Econometrics · Economics 2022-03-08 Matthew Harding , Carlos Lamarche , Chris Muris

Semi-supervised learning has received increasingly attention in statistics and machine learning. In semi-supervised learning settings, a labeled data set with both outcomes and covariates and an unlabeled data set with covariates only are…

Machine Learning · Statistics 2024-02-26 Zhuojun Quan , Yuanyuan Lin , Kani Chen , Wen Yu