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We provide a framework for modeling social network formation through conditional multinomial logit models from discrete choice and random utility theory, in which each new edge is viewed as a "choice" made by a node to connect to another…

Social and Information Networks · Computer Science 2020-05-22 Jan Overgoor , Austin R. Benson , Johan Ugander

We revisit a model for time-varying linear regression that assumes the unknown parameters evolve according to a linear dynamical system. Counterintuitively, we show that when the underlying dynamics are stable the parameters of this model…

Statistics Theory · Mathematics 2022-01-03 Ali Jadbabaie , Horia Mania , Devavrat Shah , Suvrit Sra

In this paper, we derive a central limit theorem for collections of weakly correlated random variables indexed by discrete metric spaces, where the correlation decays in the distance of the indices. The correlation structure we study…

Probability · Mathematics 2022-02-11 Michael Fleermann , Werner Kirsch

We study the asymptotic behavior of the maximum degree in the evolving tree model with a choice based on both degree and fitness of a vertex. The tree is constructed in the following recursive way. Each vertex is assigned a parameter to it…

Probability · Mathematics 2020-12-15 Yury Malyshkin

Transformers have achieved state-of-the-art results across a range of domains, but their quadratic attention mechanism poses significant challenges for long-sequence modelling. Recent efforts to design linear-time attention mechanisms have…

Computation and Language · Computer Science 2025-12-03 Rares Dolga , Lucas Maystre , Marius Cobzarenco , David Barber

We extend the feature selection methodology to dependent data and propose a novel time series predictor selection scheme that accommodates statistical dependence in a more typical i.i.d sub-sampling based framework. Furthermore, the…

Methodology · Statistics 2019-05-21 Avleen S. Bijral

Preferential attachment schemes, where the selection mechanism is linear and possibly time-dependent, are considered, and an infinite-dimensional large deviation principle for the sample path evolution of the empirical degree distribution…

Probability · Mathematics 2013-02-25 Jihyeok Choi , Sunder Sethuraman

In classical extreme value theory probabilities of extreme events are estimated assuming all the components of a random vector to be in a domain of attraction of an extreme value distribution. In contrast, the conditional extreme value…

Statistics Theory · Mathematics 2011-08-30 Bikramjit Das , Sidney I. Resnick

A variation of the preferential attachment random graph model of Barab\'asi and Albert is defined that incorporates planted communities. The graph is built progressively, with new vertices attaching to the existing ones one-by-one. At every…

Machine Learning · Statistics 2018-01-30 Bruce Hajek , Suryanarayana Sankagiri

The Conditional Preference Network (CP-net) graphically represents user's qualitative and conditional preference statements under the ceteris paribus interpretation. The constrained CP-net is an extension of the CP-net, to a set of…

Artificial Intelligence · Computer Science 2021-09-28 Sultan Ahmed , Malek Mouhoub

Joint latent class modelling has been developed considerably in the past two decades. In some instances, the models are linked by the latent class k (i.e. the number of subgroups), in others they are joined by shared random effects or a…

Methodology · Statistics 2023-03-02 Ruoyu Miao , Christiana Charalambous

We consider fixed effects binary choice models with a fixed number of periods $T$ and regressors without a large support. If the time-varying unobserved terms are i.i.d. with known distribution $F$, \cite{chamberlain2010} shows that the…

Econometrics · Economics 2022-09-30 Laurent Davezies , Xavier D'Haultfoeuille , Martin Mugnier

In the sequential change-point detection literature, most research specifies a required frequency of false alarms at a given pre-change distribution $f_{\theta}$ and tries to minimize the detection delay for every possible post-change…

Statistics Theory · Mathematics 2007-06-13 Yajun Mei

We study the problem of detecting change points (CPs) that are characterized by a subset of dimensions in a multi-dimensional sequence. A method for detecting those CPs can be formulated as a two-stage method: one for selecting relevant…

Machine Learning · Statistics 2018-03-05 Yuta Umezu , Ichiro Takeuchi

Statistical inferences for sample correlation matrices are important in high dimensional data analysis. Motivated by this, this paper establishes a new central limit theorem (CLT) for a linear spectral statistic (LSS) of high dimensional…

Statistics Theory · Mathematics 2014-11-04 Jiti Gao , Xiao Han , Guangming Pan , Yanrong Yang

Many time series exhibit changes both in level and in variability. Generally, it is more important to detect a change in the level, and changing or smoothly evolving variability can confound existing tests. This paper develops a framework…

Statistics Theory · Mathematics 2016-12-09 Tomasz Gorecki , Lajos Horvath , Piotr Kokoszka

Models with multiple change points are used in many fields; however, the theoretical properties of maximum likelihood estimators of such models have received relatively little attention. The goal of this paper is to establish the asymptotic…

Statistics Theory · Mathematics 2011-02-28 Heping He , Thomas A. Severini

The Barab\'asi-Albert model is a popular scheme for creating scale-free graphs but has been previously shown to have ambiguities in its definition. In this paper we discuss a new ambiguity in the definition of the BA model by identifying…

Data Structures and Algorithms · Computer Science 2021-10-14 Giorgos Stamatelatos , Pavlos S. Efraimidis

Change points in real-world systems mark significant regime shifts in system dynamics, possibly triggered by exogenous or endogenous factors. These points define regimes for the time evolution of the system and are crucial for understanding…

Machine Learning · Statistics 2025-09-30 Ioanna-Yvonni Tsaknaki , Fabrizio Lillo , Piero Mazzarisi

This paper proposes a flexible framework for inferring large-scale time-varying and time-lagged correlation networks from multivariate or high-dimensional non-stationary time series with piecewise smooth trends. Built on a novel and unified…

Methodology · Statistics 2023-02-13 Lujia Bai , Weichi Wu
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