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Evaluating the financial performance of manufacturing firms requires consideration of both the time value of money and the relative importance of multiple decision criteria. Conventional approaches relying solely on deterministic…

Theoretical Economics · Economics 2026-02-05 Duaa Abdullah , Marwa Abdullah

We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coefficients. The proposed methods can be used for hierarchical…

Computation · Statistics 2021-10-08 Philip Greengard , Jeremy Hoskins , Charles C. Margossian , Andrew Gelman , Aki Vehtari

We propose small-variance asymptotic approximations for the inference of tumor heterogeneity (TH) using next-generation sequencing data. Understanding TH is an important and open research problem in biology. The lack of appropriate…

Methodology · Statistics 2015-11-17 Yanxun Xu , Peter Mueller , Yuan Yuan , Kamalakar Gulukota , Yuan Ji

Cross-classified data frequently arise in scientific fields such as education, healthcare, and social sciences. A common modeling strategy is to introduce crossed random effects within a regression framework. However, this approach often…

Methodology · Statistics 2025-07-22 Shota Takeishi , Shonosuke Sugasawa

This paper considers the estimation and inference of the low-rank components in high-dimensional matrix-variate factor models, where each dimension of the matrix-variates ($p \times q$) is comparable to or greater than the number of…

Statistics Theory · Mathematics 2022-10-20 Elynn Y. Chen , Jianqing Fan

This paper studies inference in linear models with a high-dimensional parameter matrix that can be well-approximated by a ``spiked low-rank matrix.'' A spiked low-rank matrix has rank that grows slowly compared to its dimensions and nonzero…

Statistics Theory · Mathematics 2023-01-04 Victor Chernozhukov , Christian Hansen , Yuan Liao , Yinchu Zhu

In this paper we describe an algorithm for predicting the websites at risk in a long range hacking activity, while jointly inferring the provenance and evolution of vulnerabilities on websites over continuous time. Specifically, we use…

Applications · Statistics 2016-11-23 Ziqi Liu , Alexander J. Smola , Kyle Soska , Yu-Xiang Wang , Qinghua Zheng

Models implicitly defined through a random simulator of a process have become widely used in scientific and industrial applications in recent years. However, simulation-based inference methods for such implicit models, like approximate…

Methodology · Statistics 2025-04-17 Joonha Park

Inferring parameter distributions of complex industrial systems from noisy time series data requires methods to deal with the uncertainty of the underlying data and the used simulation model. Bayesian inference is well suited for these…

Applications · Statistics 2021-06-18 David N. John , Livia Stohrer , Claudia Schillings , Michael Schick , Vincent Heuveline

Likelihood-free inference methods based on neural conditional density estimation were shown to drastically reduce the simulation burden in comparison to classical methods such as ABC. When applied in the context of any latent variable…

Machine Learning · Statistics 2024-05-06 Sanmitra Ghosh , Paul J. Birrell , Daniela De Angelis

Vector autoregressive (VAR) processes are ubiquitously used in economics, finance, and biology. Order selection is an essential step in fitting VAR models. While many order selection methods exist, all come with weaknesses. Order selection…

Methodology · Statistics 2025-11-26 Michael Hellstern , Ali Shojaie

Ordinal categorical data are routinely encountered in many practical applications. When the primary goal is to construct a regression model for ordinal outcomes, cumulative link models represent one of the most popular choices to link the…

Methodology · Statistics 2026-03-13 Emanuele Aliverti

Logistic regression is an important statistical tool for assessing the probability of an outcome based upon some predictive variables. Standard methods can only deal with precisely known data, however many datasets have uncertainties which…

Methodology · Statistics 2022-06-09 Nicholas Gray , Scott Ferson

Bayesian inference with Markov Chain Monte Carlo (MCMC) is challenging when the likelihood function is irregular and expensive to compute. We explore several sampling algorithms that make use of subset evaluations to reduce computational…

Machine Learning · Statistics 2025-05-16 Conor Rosato , Harvinder Lehal , Simon Maskell , Lee Devlin , Malcolm Strens

It is an important task to model realized volatilities for high-frequency data in finance and economics and, as arguably the most popular model, the heterogeneous autoregressive (HAR) model has dominated the applications in this area.…

Methodology · Statistics 2023-03-07 Huiling Yuan , Kexin Lu , Yifeng Guo , Guodong Li

There is an especially strong need in modern large-scale data analysis to prioritize samples for manual inspection. For example, the inspection could target important mislabeled samples or key vulnerabilities exploitable by an adversarial…

Machine Learning · Statistics 2017-05-11 Mike Wojnowicz , Ben Cruz , Xuan Zhao , Brian Wallace , Matt Wolff , Jay Luan , Caleb Crable

We propose a new estimation method for heterogeneous causal effects which utilizes a regression discontinuity (RD) design for multiple datasets with different thresholds. The standard RD design is frequently used in applied researches, but…

Econometrics · Economics 2019-05-14 Takayuki Toda , Ayako Wakano , Takahiro Hoshino

In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time. We estimate the heterogeneous intercept and slope matrices via nuclear norm…

Econometrics · Economics 2022-10-21 Yiren Wang , Liangjun Su , Yichong Zhang

Both Hawkes processes and autoregressive processes rely on linear functionals of their past, while modeling different types of data. Since datasets arising from observations of the same phenomenon may be heterogeneous and sampled at…

Probability · Mathematics 2026-05-28 Théo Leblanc

We study low-rank matrix regression in settings where matrix-valued predictors and scalar responses are observed across multiple individuals. Rather than assuming a fully homogeneous coefficient matrices across individuals, we accommodate…

Methodology · Statistics 2025-10-28 Di Wang , Xiaoyu Zhang , Guodong Li , Wenyang Zhang