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Related papers: Triple-dyad ratio estimation for the $p_1$ model

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Distributed statistical inference has recently attracted immense attention. The asymptotic efficiency of the maximum likelihood estimator (MLE), the one-step MLE, and the aggregated estimating equation estimator are established for…

Methodology · Statistics 2020-08-14 Ping Zhou , Zhen Yu , Jingyi Ma , Maozai Tian , Ye Fan

We propose a general approach to construct weighted likelihood estimating equations with the aim of obtaining robust parameter estimates. We modify the standard likelihood equations by incorporating a weight that reflects the statistical…

Statistics Theory · Mathematics 2025-07-24 Claudio Agostinelli , Ayanendranath Basu , Giulia Bertagnolli , Arun Kumar Kuchibhotla

We consider ordered logit models for directed network data that allow for flexible sender and receiver fixed effects that can vary arbitrarily across outcome categories. This structure poses a significant incidental parameter problem,…

Econometrics · Economics 2025-07-23 Chris Muris , Cavit Pakel , Qichen Zhang

We study and compare three estimators of a discrete monotone distribution: (a) the (raw) empirical estimator; (b) the "method of rearrangements" estimator; and (c) the maximum likelihood estimator. We show that the maximum likelihood…

Statistics Theory · Mathematics 2009-10-20 Hanna K. Jankowski , Jon A. Wellner

Estimating network formation models with degree heterogeneity raises two problems in empirical networks. First, agents that send no links, receive no links, or link to all remaining agents can make the fixed-effects MLE fail to exist.…

Econometrics · Economics 2026-05-04 Zizhong Yan , Jingrong Li , Yi Zhang

In this paper, we develop asymptotic theories for a class of latent variable models for large-scale multi-relational networks. In particular, we establish consistency results and asymptotic error bounds for the (penalized) maximum…

Statistics Theory · Mathematics 2020-09-01 Zhi Wang , Xueying Tang , Jingchen Liu

This paper develops a quasi-maximum likelihood estimator for genuinely unbalanced dynamic network panel data models with individual fixed effects. We propose a model that accommodates contemporaneous and lagged network spillovers, temporal…

Methodology · Statistics 2026-01-01 Zhijian Wang , Xingbai Xu , Tuo Liu

Maximum Likelihood Estimators (MLE) has many good properties. For example, the asymptotic variance of MLE solution attains equality of the asymptotic Cram{\'e}r-Rao lower bound (efficiency bound), which is the minimum possible variance for…

Machine Learning · Statistics 2019-11-05 Song Liu , Takafumi Kanamori , Wittawat Jitkrittum , Yu Chen

When estimating a proportion and only a sample of triplets is given, dependencies within the triplets are to be accounted for. Without assuming a distribution for the success count of the triplet, together with the proportion, as second and…

Methodology · Statistics 2022-03-11 Rafael Weissbach , Eric Scholz

We are concerned here with unrestricted maximum likelihood estimation in a sparse $p_0$ model with covariates for directed networks. The model has a density parameter $\nu$, a $2n$-dimensional node parameter $\bs{\eta}$ and a fixed…

Statistics Theory · Mathematics 2021-06-08 Qiuping Wang

Suppose one has a collection of parameters indexed by a (possibly infinite dimensional) set. Given data generated from some distribution, the objective is to estimate the maximal parameter in this collection evaluated at this distribution.…

Methodology · Statistics 2016-05-26 Alexander R. Luedtke , Mark J. van der Laan

According to standard econometric theory, Maximum Likelihood estimation (MLE) is the efficient estimation choice, however, it is not always a feasible one. In network diffusion models with unobserved signal propagation, MLE requires…

Econometrics · Economics 2023-09-06 L. S. Sanna Stephan

Motivated by the home-field advantage in sports, we propose a generalized Bradley--Terry model that incorporates covariate information for paired comparisons. It has an $n$-dimensional merit parameter $\bs{\beta}$ and a fixed-dimensional…

Methodology · Statistics 2025-07-31 Ting Yan

Density ratio estimation (DRE) is a paramount task in machine learning, for its broad applications across multiple domains, such as covariate shift adaptation, causal inference, independence tests and beyond. Parametric methods for…

Machine Learning · Statistics 2025-06-03 Meilin Wang , Wei Huang , Mingming Gong , Zheng Zhang

Machine learning methods, particularly the double machine learning (DML) estimator (Chernozhukov et al., 2018), are increasingly popular for the estimation of the average treatment effect (ATE). However, datasets often exhibit unbalanced…

Econometrics · Economics 2024-06-12 Daniele Ballinari

In fitting a mixture of linear regression models, normal assumption is traditionally used to model the error and then regression parameters are estimated by the maximum likelihood estimators (MLE). This procedure is not valid if the normal…

Methodology · Statistics 2018-11-06 Yanyuan Ma , Shaoli Wang , Lin Xu , Weixin Yao

Standard maximum likelihood estimation cannot be applied to discrete energy-based models in the general case because the computation of exact model probabilities is intractable. Recent research has seen the proposal of several new…

Machine Learning · Computer Science 2012-02-20 Benjamin Marlin , Nando de Freitas

The parameter estimation of unnormalized models is a challenging problem. The maximum likelihood estimation (MLE) is computationally infeasible for these models since normalizing constants are not explicitly calculated. Although some…

Machine Learning · Statistics 2020-06-09 Masatoshi Uehara , Takafumi Kanamori , Takashi Takenouchi , Takeru Matsuda

We propose a semiparametric model for dyadic link formations in directed networks. The model contains a set of degree parameters that measure different effects of popularity or outgoingness across nodes, a regression parameter vector that…

Methodology · Statistics 2025-02-21 Lianqiang Qu , Lu Chen , Ting Yan , Yuguo Chen

Reciprocity--the tendency of individuals to form mutual ties--is a fundamental structural feature of many directed networks. Despite its ubiquity, reciprocity remains insufficiently integrated into statistical network models, particularly…

Methodology · Statistics 2025-07-30 Rui Feng , Chenlei Leng
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