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Novel Semiparametric Methods
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Novel Semiparametric Methods

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 14558

Special Issue Editor

Dr. Eddy Kwessi

E-Mail Website
Guest Editor
Department of Mathematics, Trinity University, 115 E Mars McLean, San Antonio, TX 78212, USA
Interests: nonparametric statistics; machine learning; computational neuroscience; computational dynamical systems; functional data analysis; harmonic and functional analysis with applications to data analysis

Special Issue Information

Dear Colleagues,

It is my pleasure to announce a Special Issue entitled “Novel Semiparametric Methods”. In this era of big data, there is evidence that some of the traditional methods of analysis of data do not always address the complexity of the data. Novel methods in semiparametric analysis include, but are not limited to, wavelets, orthogonal polynomial, and nontraditional bases to address the intricacies of nonlinear parts of semiparametric models. Additionally, estimates of linear parts using robust methods, such as quantiles, M-estimation, or rank estimation, with the hope of proposing robust combinations for functional data analysis, would be highly considered. Manuscripts with applications to economy, biology, finance, engineering, public heath would be appreciated. Special attention will be given to manuscripts addressing the statistical issues of prediction in COVID-19 models.

I look forward to receiving your submissions.

Dr. Eddy Kwessi
Guest Editor

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. Stats is an international peer-reviewed open access quarterly 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 1600 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.

Dr. Eddy Kwessi
Guest Editor

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. Stats is an international peer-reviewed open access quarterly 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 1600 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

  • Semiparametric
  • methods
  • nonlinear
  • robust
  • quantile
  • M-estimation
  • Ranks
  • wavelets
  • orthogonal polynomial
  • COVID-19

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Published Papers (7 papers)

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Research

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20 pages, 429 KiB  
Article
Doubly Robust Estimation and Semiparametric Efficiency in Generalized Partially Linear Models with Missing Outcomes
by Lu Wang, Zhongzhe Ouyang and Xihong Lin
Stats 2024, 7(3), 924-943; https://doi.org/10.3390/stats7030056 - 31 Aug 2024
Viewed by 1346
Abstract
We investigate a semiparametric generalized partially linear regression model that accommodates missing outcomes, with some covariates modeled parametrically and others nonparametrically. We propose a class of augmented inverse probability weighted (AIPW) kernel–profile estimating equations. The nonparametric component is estimated using AIPW kernel estimating [...] Read more.
We investigate a semiparametric generalized partially linear regression model that accommodates missing outcomes, with some covariates modeled parametrically and others nonparametrically. We propose a class of augmented inverse probability weighted (AIPW) kernel–profile estimating equations. The nonparametric component is estimated using AIPW kernel estimating equations, while parametric regression coefficients are estimated using AIPW profile estimating equations. We demonstrate the doubly robust nature of the AIPW estimators for both nonparametric and parametric components. Specifically, these estimators remain consistent if either the assumed model for the probability of missing data or that for the conditional mean of the outcome, given covariates and auxiliary variables, is correctly specified, though not necessarily both simultaneously. Additionally, the AIPW profile estimator for parametric regression coefficients is consistent and asymptotically normal under the semiparametric model defined by the generalized partially linear model on complete data, assuming that the missing data mechanism is missing at random. When both working models are correctly specified, this estimator achieves semiparametric efficiency, with its asymptotic variance reaching the efficiency bound. We validate our approach through simulations to assess the finite sample performance of the proposed estimators and apply the method to a study that investigates risk factors associated with myocardial ischemia. Full article
(This article belongs to the Special Issue Novel Semiparametric Methods)
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20 pages, 1233 KiB  
Article
Precise Tensor Product Smoothing via Spectral Splines
by Nathaniel E. Helwig
Stats 2024, 7(1), 34-53; https://doi.org/10.3390/stats7010003 - 10 Jan 2024
Cited by 2 | Viewed by 2218
Abstract
Tensor product smoothers are frequently used to include interaction effects in multiple nonparametric regression models. Current implementations of tensor product smoothers either require using approximate penalties, such as those typically used in generalized additive models, or costly parameterizations, such as those used in [...] Read more.
Tensor product smoothers are frequently used to include interaction effects in multiple nonparametric regression models. Current implementations of tensor product smoothers either require using approximate penalties, such as those typically used in generalized additive models, or costly parameterizations, such as those used in smoothing spline analysis of variance models. In this paper, I propose a computationally efficient and theoretically precise approach for tensor product smoothing. Specifically, I propose a spectral representation of a univariate smoothing spline basis, and I develop an efficient approach for building tensor product smooths from marginal spectral spline representations. The developed theory suggests that current tensor product smoothing methods could be improved by incorporating the proposed tensor product spectral smoothers. Simulation results demonstrate that the proposed approach can outperform popular tensor product smoothing implementations, which supports the theoretical results developed in the paper. Full article
(This article belongs to the Special Issue Novel Semiparametric Methods)
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13 pages, 13506 KiB  
Article
Detecting Regional Differences in Italian Health Services during Five COVID-19 Waves
by Lucio Palazzo and Riccardo Ievoli
Stats 2023, 6(2), 506-518; https://doi.org/10.3390/stats6020032 - 15 Apr 2023
Cited by 1 | Viewed by 1852
Abstract
During the waves of the COVID-19 pandemic, both national and/or territorial healthcare systems have been severely stressed in many countries. The availability (and complexity) of data requires proper comparisons for understanding differences in the performance of health services. With this aim, we propose [...] Read more.
During the waves of the COVID-19 pandemic, both national and/or territorial healthcare systems have been severely stressed in many countries. The availability (and complexity) of data requires proper comparisons for understanding differences in the performance of health services. With this aim, we propose a methodological approach to compare the performance of the Italian healthcare system at the territorial level, i.e., considering NUTS 2 regions. Our approach consists of three steps: the choice of a distance measure between available time series, the application of weighted multidimensional scaling (wMDS) based on this distance, and, finally, a cluster analysis on the MDS coordinates. We separately consider daily time series regarding the deceased, intensive care units, and ordinary hospitalizations of patients affected by COVID-19. The proposed procedure identifies four clusters apart from two outlier regions. Changes between the waves at a regional level emerge from the main results, allowing the pressure on territorial health services to be mapped between 2020 and 2022. Full article
(This article belongs to the Special Issue Novel Semiparametric Methods)
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15 pages, 920 KiB  
Article
Farlie–Gumbel–Morgenstern Bivariate Moment Exponential Distribution and Its Inferences Based on Concomitants of Order Statistics
by Sasikumar Padmini Arun, Christophe Chesneau, Radhakumari Maya and Muhammed Rasheed Irshad
Stats 2023, 6(1), 253-267; https://doi.org/10.3390/stats6010015 - 3 Feb 2023
Cited by 6 | Viewed by 2132
Abstract
In this research, we design the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution, a bivariate analogue of the moment exponential distribution, using the Farlie–Gumbel–Morgenstern approach. With the analysis of real-life data, the competitiveness of the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution in comparison with the other [...] Read more.
In this research, we design the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution, a bivariate analogue of the moment exponential distribution, using the Farlie–Gumbel–Morgenstern approach. With the analysis of real-life data, the competitiveness of the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution in comparison with the other Farlie–Gumbel–Morgenstern distributions is discussed. Based on the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution, we develop the distribution theory of concomitants of order statistics and derive the best linear unbiased estimator of the parameter associated with the variable of primary interest (study variable). Evaluations are also conducted regarding the efficiency comparison of the best linear unbiased estimator relative to the respective unbiased estimator. Additionally, empirical illustrations of the best linear unbiased estimator with respect to the unbiased estimator are performed. Full article
(This article belongs to the Special Issue Novel Semiparametric Methods)
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13 pages, 1103 KiB  
Article
Robust Testing of Paired Outcomes Incorporating Covariate Effects in Clustered Data with Informative Cluster Size
by Sandipan Dutta
Stats 2022, 5(4), 1321-1333; https://doi.org/10.3390/stats5040080 - 14 Dec 2022
Cited by 2 | Viewed by 1702
Abstract
Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a [...] Read more.
Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a cluster size) is correlated to the paired outcomes or the paired differences. There have been some attempts to develop robust rank-based tests for comparing paired outcomes in such complex clustered data. Most of these existing rank tests developed for paired outcomes in clustered data compare the marginal distributions in a pair and ignore any covariate effect on the outcomes. However, when potentially important covariate data is available in observational studies, ignoring these covariate effects on the outcomes can result in a flawed inference. In this article, using rank based weighted estimating equations, we propose a robust procedure for covariate effect adjusted comparison of paired outcomes in a clustered data that can also address the issue of informative cluster size. Through simulated scenarios and real-life neuroimaging data, we demonstrate the importance of considering covariate effects during paired testing and robust performances of our proposed method in covariate adjusted paired comparisons in complex clustered data settings. Full article
(This article belongs to the Special Issue Novel Semiparametric Methods)
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12 pages, 712 KiB  
Article
Modeling Secondary Phenotypes Conditional on Genotypes in Case–Control Studies
by Naomi C. Brownstein, Jianwen Cai, Shad Smith, Luda Diatchenko, Gary D. Slade and Eric Bair
Stats 2022, 5(1), 203-214; https://doi.org/10.3390/stats5010014 - 22 Feb 2022
Viewed by 3442
Abstract
Traditional case–control genetic association studies examine relationships between case–control status and one or more covariates. It is becoming increasingly common to study secondary phenotypes and their association with the original covariates. The Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) project, a study [...] Read more.
Traditional case–control genetic association studies examine relationships between case–control status and one or more covariates. It is becoming increasingly common to study secondary phenotypes and their association with the original covariates. The Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) project, a study of temporomandibular disorders (TMD), motivates this work. Numerous measures of interest are collected at enrollment, such as the number of comorbid pain conditions from which a participant suffers. Examining the potential genetic basis of these measures is of secondary interest. Assessing these associations is statistically challenging, as participants do not form a random sample from the population of interest. Standard methods may be biased and lack coverage and power. We propose a general method for the analysis of arbitrary phenotypes utilizing inverse probability weighting and bootstrapping for standard error estimation. The method may be applied to the complicated association tests used in next-generation sequencing studies, such as analyses of haplotypes with ambiguous phase. Simulation studies show that our method performs as well as competing methods when they are applicable and yield promising results for outcome types, such as time-to-event, to which other methods may not apply. The method is applied to the OPPERA baseline case–control genetic study. Full article
(This article belongs to the Special Issue Novel Semiparametric Methods)
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Other

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16 pages, 520 KiB  
Case Report
Parametric Estimation in Fractional Stochastic Differential Equation
by Paramahansa Pramanik, Edward L. Boone and Ryad A. Ghanam
Stats 2024, 7(3), 745-760; https://doi.org/10.3390/stats7030045 - 20 Jul 2024
Cited by 2 | Viewed by 1045
Abstract
Fractional Stochastic Differential Equations are becoming more popular in the literature as they can model phenomena in financial data that typical Stochastic Differential Equations models cannot. In the formulation considered here, the Hurst parameter, H, controls the Fraction of Differentiation, which needs [...] Read more.
Fractional Stochastic Differential Equations are becoming more popular in the literature as they can model phenomena in financial data that typical Stochastic Differential Equations models cannot. In the formulation considered here, the Hurst parameter, H, controls the Fraction of Differentiation, which needs to be estimated from the data. Fortunately, the covariance structure among observations in time is easily expressed in terms of the Hurst parameter which means that a likelihood is easily defined. This work derives the Maximum Likelihood Estimator for H, which shows that it is biased and is not a consistent estimator. Simulation data used to understand the bias of the estimator is used to create an empirical bias correction function and a bias-corrected estimator is proposed and studied. Via simulation, the bias-corrected estimator is shown to be minimally biased and its simulation-based standard error is created, which is then used to create a 95% confidence interval for H. A simulation study shows that the 95% confidence intervals have decent coverage probabilities for large n. This method is then applied to the S&P500 and VIX data before and after the 2008 financial crisis. Full article
(This article belongs to the Special Issue Novel Semiparametric Methods)
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