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In this paper we study the least squares (LS) estimator in a linear panel regression model with unknown number of factors appearing as interactive fixed effects. Assuming that the number of factors used in estimation is larger than the true…

Econometrics · Economics 2026-05-04 Hyungsik Roger Moon , Martin Weidner

We prove the Fundamental Theorem of Asset Pricing for a discrete time financial market where trading is subject to proportional transaction cost and the asset price dynamic is modeled by a family of probability measures, possibly…

Probability · Mathematics 2015-09-01 Erhan Bayraktar , Yuchong Zhang

Aligning large language models (LLMs) to preference data typically assumes a known link function between observed preferences and latent rewards (e.g., a logistic Bradley-Terry link). Misspecification of this link can bias inferred rewards…

Machine Learning · Computer Science 2026-02-03 Nathan Kallus

We propose a Fundamental Theorem of Asset Pricing and a Super-Replication Theorem in a model-independent framework. We prove these theorems in the setting of finite, discrete time and a market consisting of a risky asset S as well as…

Probability · Mathematics 2013-03-27 Beatrice Acciaio , Mathias Beiglböck , Friedrich Penkner , Walter Schachermayer

This work is devoted to the finite sample prediction risk analysis of a class of linear predictors of a response $Y\in \mathbb{R}$ from a high-dimensional random vector $X\in \mathbb{R}^p$ when $(X,Y)$ follows a latent factor regression…

Machine Learning · Statistics 2021-04-26 Xin Bing , Florentina Bunea , Seth Strimas-Mackey , Marten Wegkamp

This paper introduces yet another stochastic model replicating chain-ladder estimates and furthermore considers extensions that add flexibility to the modeling. In its simplest form, the proposed model replicates the chain-ladder's…

Applications · Statistics 2025-01-03 Gabriele Pittarello , Munir Hiabu , Andrés M. Villegas

We consider identification, inference and validation of linear panel data models when both factors and factor loadings are accounted for by a nonparametric function. This general specification encompasses rather popular models such as the…

Econometrics · Economics 2025-06-13 Juan M. Rodriguez-Poo , Alexandra Soberon , Stefan Sperlich

Stochastic discount factor (SDF) processes in dynamic economies admit a permanent-transitory decomposition in which the permanent component characterizes pricing over long investment horizons. This paper introduces an empirical framework to…

Methodology · Statistics 2022-06-06 Timothy Christensen

In this paper a multi-factor generalization of Ho-Lee model is proposed. In sharp contrast to the classical Ho-Lee, this generalization allows for those movements other than parallel shifts, while it still is described by a recombining…

Probability · Mathematics 2008-12-02 Jirô Akahori , Hiroki Aoki , Yoshihiko Nagata

Motivated by an application in computational biology, we consider low-rank matrix factorization with $\{0,1\}$-constraints on one of the factors and optionally convex constraints on the second one. In addition to the non-convexity shared…

Machine Learning · Statistics 2014-01-24 Martin Slawski , Matthias Hein , Pavlo Lutsik

Reduced-rank decompositions provide descriptions of the variation among the elements of a matrix or array. In such decompositions, the elements of an array are expressed as products of low-dimensional latent factors. This article presents a…

Methodology · Statistics 2010-06-01 Peter Hoff

The model implied by factor score predictors does not reproduce the non-diagonal elements of the observed covariance matrix as well as the factor loadings. It is therefore investigated whether it is possible to estimate factor loadings for…

Applications · Statistics 2019-09-26 André Beauducel , Norbert Hilger

Financial prediction from long documents involves significant challenges, as actionable signals are often sparse and obscured by noise, and the optimal LLM for generating embeddings varies across tasks and time periods. In this paper, we…

Computation and Language · Computer Science 2026-02-25 Zirui He , Huopu Zhang , Yanguang Liu , Sirui Wu , Mengnan Du

With the proliferation of knowledge graphs, modeling data with complex multirelational structure has gained increasing attention in the area of statistical relational learning. One of the most important goals of statistical relational…

Machine Learning · Computer Science 2021-11-10 Ye Liu , Rui Song , Wenbin Lu , Yanghua Xiao

This paper presents a factor analysis model for symbolic data, focusing on the particular case of interval-valued variables. The proposed method describes the correlation structure among the measured interval-valued variables in terms of a…

Methodology · Statistics 2017-09-15 Paula Cheira , Paula Brito , A. Pedro Duarte Silva

Dynamic pricing in high-dimensional markets poses fundamental challenges of scalability, uncertainty, and interpretability. Existing low-rank bandit formulations learn efficiently but rely on latent features that obscure how individual…

Artificial Intelligence · Computer Science 2026-02-03 Srividhya Sethuraman , Chandrashekar Lakshminarayanan

Estimates of the approximate factor model are increasingly used in empirical work. Their theoretical properties, studied some twenty years ago, also laid the ground work for analysis on large dimensional panel data models with cross-section…

Econometrics · Economics 2020-08-04 Jushan Bai , Serena Ng

We design a system for risk-analyzing and pricing portfolios of non-performing consumer credit loans. The rapid development of credit lending business for consumers heightens the need for trading portfolios formed by overdue loans as a…

Risk Management · Quantitative Finance 2021-10-29 Siyi Wang , Xing Yan , Bangqi Zheng , Hu Wang , Wangli Xu , Nanbo Peng , Qi Wu

Many economic environments involve units linked by a network. I develop an econometric framework that derives the dynamics of cross-sectional variables from the lagged innovation transmission along bilateral links and that can accommodate…

Econometrics · Economics 2026-01-23 Marko Mlikota

Kolmogorov's representation theorem provides a framework for decomposing any arbitrary real-valued, multivariate, and continuous function into a two-layer nested superposition of a finite number of functions. The functions at these two…

Information Theory · Computer Science 2021-05-18 Derya Malak , Ali Tajer
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