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We propose a new method for modelling simple longitudinal data. We aim to do this in a flexible manner (without restrictive assumptions about the shapes of individual trajectories), while exploiting structural similarities between the…

Methodology · Statistics 2024-09-24 Helen Ogden

Missing data is a common problem in clinical data collection, which causes difficulty in the statistical analysis of such data. In this article, we consider the problem under a framework of a semiparametric partially linear model when…

Methodology · Statistics 2022-06-13 Zishu Zhan , Xiangjie Li , Jingxiao Zhang

It has been some time since interval-valued linear regression was investigated. In this paper, we focus on linear regression for interval-valued data within the framework of random sets. The model we propose generalizes a series of existing…

Methodology · Statistics 2015-06-12 Yan Sun , Chunyang Li

In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention…

Econometrics · Economics 2021-04-12 Ricardo P. Masini , Marcelo C. Medeiros , Eduardo F. Mendes

Interval-valued data receives much attention due to its wide applications in the fields of finance, econometrics, meteorology and medicine. However, most regression models developed for interval-valued data assume observations are mutually…

Applications · Statistics 2022-10-31 Tingting Huang

Functional data analysis is proved to be useful in many scientific applications. The physical process is observed as curves and often there are several curves observed due to multiple subjects, providing the replicates in statistical sense.…

Methodology · Statistics 2018-01-30 Tapabrata Maiti , Abolfazl Safikhani , Ping-Shou Zhong

Traditional nonparametric estimation methods often lead to a slow convergence rate in large dimensions and require unrealistically enormous sizes of datasets for reliable conclusions. We develop an approach based on partial derivatives,…

Methodology · Statistics 2024-08-20 Xiaowu Dai

Finite mixture distributions arise in sampling a heterogeneous population. Data drawn from such a population will exhibit extra variability relative to any single subpopulation. Statistical models based on finite mixtures can assist in the…

Methodology · Statistics 2024-01-19 Andrew M. Raim , Nagaraj K. Neerchal , Jorge G. Morel

An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and the inference of…

Optimal designs for generalized linear models require a prior knowledge of the regression parameters. At certain values of the parameters we propose particular assumptions which allow to derive a locally optimal design for a model without…

Statistics Theory · Mathematics 2019-06-26 Osama Idais

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 address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models. We start from the case of a single observational dataset…

Artificial Intelligence · Computer Science 2023-03-17 Marco Zaffalon , Alessandro Antonucci , David Huber , Rafael Cabañas

The linear model, in which a set of observations is assumed to be given by a linear combination of columns of a matrix, has long been the mainstay of the statistics and signal processing literature. One particular challenge for inference…

Statistics Theory · Mathematics 2018-03-06 Waheed U. Bajwa , Marco F. Duarte , Robert Calderbank

Probabilistic models help us encode latent structures that both model the data and are ideally also useful for specific downstream tasks. Among these, mixture models and their time-series counterparts, hidden Markov models, identify…

Machine Learning · Computer Science 2021-10-29 Abhishek Sharma , Catherine Zeng , Sanjana Narayanan , Sonali Parbhoo , Finale Doshi-Velez

Many causal and structural parameters in economics can be identified and estimated by computing the value of an optimization program over all distributions consistent with the model and the data. Existing tools apply when the data is…

Econometrics · Economics 2025-07-31 Andrei Voronin

Combining information from multiple samples is often needed in biomedical and economic studies, but the differences between these samples must be appropriately taken into account in the analysis of the combined data. We study estimation for…

Methodology · Statistics 2018-08-14 Heng Shu , Zhiqiang Tan

Generalized linear models are flexible tools for the analysis of diverse datasets, but the classical formulation requires that the parametric component is correctly specified and the data contain no atypical observations. To address these…

Methodology · Statistics 2023-04-21 Ioannis Kalogridis , Gerda Claeskens , Stefan Van Aelst

We study the problem of learning clusters of partially observed linear dynamical systems from multiple input-output trajectories. This setting is particularly relevant when there are limited observations (e.g., short trajectories) from…

Systems and Control · Electrical Eng. & Systems 2025-07-24 Maryann Rui , Munther A. Dahleh

Lacking lifetime income data, most intergenerational mobility estimates are subject to lifecycle bias. Using long income series from Sweden and the US, we illustrate that standard correction methods struggle to account for one important…

General Economics · Economics 2025-12-18 Ursula Mello , Martin Nybom , Jan Stuhler

Big data analytics has opened new avenues in economic research, but the challenge of analyzing datasets with tens of millions of observations is substantial. Conventional econometric methods based on extreme estimators require large amounts…

Econometrics · Economics 2023-11-02 Sokbae Lee , Yuan Liao , Myung Hwan Seo , Youngki Shin
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