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Mathematics
Special Issues
New Advances in Statistics and Econometrics
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New Advances in Statistics and Econometrics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 1887

Special Issue Editors

Dr. Zheng Xu

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, Wright State University, Dayton, OH 45435, USA
Interests: machine learning; deep learning; biostatistics; bioinformatics; genetic epidemiology; nonparametric and semi-parametric methods; econometrics; financial statistics; prediction; agricultural statistics; computer vision; statistical inference; statistical applications
Dr. Sixia Chen

E-Mail Website
Guest Editor
Department of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
Interests: missing data analysis; survey sampling; empirical likelihood; machine learning methods; causal inference; biostatistics; American Indian health disparities; tobacco research; cancer prevention and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of scientific techniques, statistics and econometrics have been developed with new methods and analyses. Statistics and econmetrics have been quickly advancing to better analyze more complex and massive data. Advances in statistics and econometrics have been made in a range of areas from biostatistics, bioinformatics,  biology, health sciences, biological, agricultural economics, finance, business, power, and electricity markets. To better analyze data in the fast-changing world, statistics and econometrics have also been advanced to combine deep learning, machine learning, computing, computer vision, image analysis, image phenotyping, survey, prediction, modeling, missing data, etc.

In this Special Issue, we invite high-quality research papers on new advances in statistics and econometrics. We invite investigators to contribute original research articles as well as review articles that will advance statistical and econometric methodologies with data analysis applications.

Dr. Zheng Xu
Dr. Sixia Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning and deep learning
  • biostatistics
  • bioinformatics
  • econometrics
  • health statistics
  • financial statistics
  • financial markets
  • next-generation sequencing data analysis
  • statistical genetics
  • association studies
  • computational statistics
  • image analysis and computer vision
  • survey sampling
  • novel statistical methods with applications
  • novel econometric methods with applications

Published Papers (1 paper)

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Research

14 pages, 1254 KiB  
Article
The Concavity of Conditional Maximum Likelihood Estimation for Logit Panel Data Models with Imputed Covariates
by Opeyo Peter Otieno and Weihu Cheng
Mathematics 2023, 11(20), 4338; https://doi.org/10.3390/math11204338 - 18 Oct 2023
Viewed by 1463
Abstract
In estimating logistic regression models, convergence of the maximization algorithm is critical; however, this may fail. Numerous bias correction methods for maximum likelihood estimates of parameters have been conducted for cases of complete data sets, and also for longitudinal models. Balanced data sets [...] Read more.
In estimating logistic regression models, convergence of the maximization algorithm is critical; however, this may fail. Numerous bias correction methods for maximum likelihood estimates of parameters have been conducted for cases of complete data sets, and also for longitudinal models. Balanced data sets yield consistent estimates from conditional logit estimators for binary response panel data models. When faced with a missing covariates problem, researchers adopt various imputation techniques to complete the data and without loss of generality; consistent estimates still suffice asymptotically. For maximum likelihood estimates of the parameters for logistic regression in cases of imputed covariates, the optimal choice of an imputation technique that yields the best estimates with minimum variance is still elusive. This paper aims to examine the behaviour of the Hessian matrix with optimal values of the imputed covariates vector, which will make the Newton–Raphson algorithm converge faster through a reduced absolute value of the product of the score function and the inverse fisher information component. We focus on a method used to modify the conditional likelihood function through the partitioning of the covariate matrix. We also confirm that the positive moduli of the Hessian for conditional estimators are sufficient for the concavity of the log-likelihood function, resulting in optimum parameter estimates. An increased Hessian modulus ensures the faster convergence of the parameter estimates. Simulation results reveal that model-based imputations perform better than classical imputation techniques, yielding estimates with smaller bias and higher precision for the conditional maximum likelihood estimation of nonlinear panel models. Full article
(This article belongs to the Special Issue New Advances in Statistics and Econometrics)
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