多元面板数据的隐马尔可夫模型

IF 1.6 2区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Statistics and Computing Pub Date : 2024-09-18 DOI:10.1007/s11222-024-10462-0

Mackenzie R. Neal, Alexa A. Sochaniwsky, Paul D. McNicholas

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引用次数: 0

摘要

尽管基于模型的聚类技术不断进步，但在对面板数据等各种数据类型进行建模时仍面临挑战。多变量面板数据给聚类算法带来了困难，因为它们经常受到缺失数据和遗漏数据的困扰，给估计算法带来了问题。本研究提出了一系列隐马尔可夫模型，以弥补面板数据中出现的问题。本文提出了一种能够处理非随机数据缺失和遗漏的修正期望最大化算法，并将其用于模型估计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Hidden Markov models for multivariate panel data

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Hidden Markov models for multivariate panel data

While advances continue to be made in model-based clustering, challenges persist in modeling various data types such as panel data. Multivariate panel data present difficulties for clustering algorithms because they are often plagued by missing data and dropouts, presenting issues for estimation algorithms. This research presents a family of hidden Markov models that compensate for the issues that arise in panel data. A modified expectation–maximization algorithm capable of handling missing not at random data and dropout is presented and used to perform model estimation.

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来源期刊

Statistics and Computing 数学-计算机：理论方法

CiteScore

3.20

自引率

4.50%

发文量

审稿时长

6-12 weeks

期刊介绍： Statistics and Computing is a bi-monthly refereed journal which publishes papers covering the range of the interface between the statistical and computing sciences. In particular, it addresses the use of statistical concepts in computing science, for example in machine learning, computer vision and data analytics, as well as the use of computers in data modelling, prediction and analysis. Specific topics which are covered include: techniques for evaluating analytically intractable problems such as bootstrap resampling, Markov chain Monte Carlo, sequential Monte Carlo, approximate Bayesian computation, search and optimization methods, stochastic simulation and Monte Carlo, graphics, computer environments, statistical approaches to software errors, information retrieval, machine learning, statistics of databases and database technology, huge data sets and big data analytics, computer algebra, graphical models, image processing, tomography, inverse problems and uncertainty quantification. In addition, the journal contains original research reports, authoritative review papers, discussed papers, and occasional special issues on particular topics or carrying proceedings of relevant conferences. Statistics and Computing also publishes book review and software review sections.