Recent Advances of Computational Statistics in Industry and Business

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

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 21361

Special Issue Editor


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Guest Editor

Special Issue Information

Dear Colleagues,

Computational statistics emphasizes algorithms and numerical methods and plays an essential role in the areas of industry, science, and business. Many novel CS methods have been proposed in the past decade. Numerous researchers and technicians have dedicated their time to studying novel CS methods and using CS methods to deal with data in various fields, such as engineering, reliability, economics, business, medicine, biology, and physics. The main purpose of this Special Issue is to provide a collection of manuscripts that propose novel CS methods for statistical inference or using CS methods for simulations and relevant case studies. Potential topics including:

  • Reliability modeling and testing
  • Software reliability and testing
  • Modeling analysis and simulation
  • Quality assurance and cost issues
  • Quality engineering
  • Life testing
  • Optimization and simulation
  • Maintainability and availability
  • Methodologies for quality control
  • Big data in economics
  • Big data in business

Prof. Dr. Tzong-Ru Tsai
Guest Editor

Manuscript Submission Information

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Keywords

  • Reliability
  • Statistical process control
  • Big data
  • Preventive maintenance
  • Bayesian estimation
  • Machine learning

Published Papers (8 papers)

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Research

14 pages, 444 KiB  
Article
The Pet Affection Scale Development, Validation and Influence on Consumers’ Behavior of Pet Hotels
by Yung-Hsin Lee and Chih-Min Lai
Mathematics 2021, 9(15), 1772; https://doi.org/10.3390/math9151772 - 27 Jul 2021
Cited by 1 | Viewed by 5095
Abstract
The purpose of this research was to develop a measurement scale, the Pet Affection Scale (PAS), to understand owners’ personalities and attachment to their pets. The data were collected through two waves. There were 401 valid data collected from the first wave to [...] Read more.
The purpose of this research was to develop a measurement scale, the Pet Affection Scale (PAS), to understand owners’ personalities and attachment to their pets. The data were collected through two waves. There were 401 valid data collected from the first wave to develop the pet affection scale (PAS). An exploratory factor analysis (EFA) was tested, and three factors were extracted and identified as (1) joy, (2) anthropomorphism, and (3) protection, respectively. Furthermore, 901 valid data collected from the second wave were used to analyze and propose a research model to examine the PAS influence on the owners’ behavioral intention toward pet hotels. These research findings show that all three pet affections have positive significant effects on pet hotels’ behavioral intention. The implications, limitations, and future research of this research were suggested and discussed. Full article
(This article belongs to the Special Issue Recent Advances of Computational Statistics in Industry and Business)
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15 pages, 312 KiB  
Article
Fuzzy Portfolio Selection in COVID-19 Spreading Period Using Fuzzy Goal Programming Model
by Ruey-Chyn Tsaur, Chien-Liang Chiu and Yin-Yin Huang
Mathematics 2021, 9(8), 835; https://doi.org/10.3390/math9080835 - 12 Apr 2021
Cited by 5 | Viewed by 1810
Abstract
While the international lockdown for the COVID-19 pandemic has greatly influenced the global economy, we are still confronted with the dilemma about the economy recession when the stock market hits record highs repeatedly. As we know, since portfolio selection is often affected by [...] Read more.
While the international lockdown for the COVID-19 pandemic has greatly influenced the global economy, we are still confronted with the dilemma about the economy recession when the stock market hits record highs repeatedly. As we know, since portfolio selection is often affected by many non-probabilistic factors, it is of not easiness to obtain the precise probability distributions of the return rates. Therefore, fuzzy portfolio model is proposed for solving the efficient portfolio when the economy environment remains in vagueness for the future. To manage such an investment, we revise the Chen and Tsaur’s fuzzy portfolio model by using fuzzy goal programming model. Then, two numerical examples are illustrated by the proposed model which shows that the portfolio selection can be solved by the linguistic imprecise goal of the expected return. With different linguistic descriptions for the imprecise goal of expected return for the future stock market, the optimal portfolio selection that can be solved under different investment risks which is considered better than Chen and Tsaur’s model in real world situations. The sensitivity analysis with some parameter groups can be provided for more invested risk selection in the portfolio analysis. Full article
(This article belongs to the Special Issue Recent Advances of Computational Statistics in Industry and Business)
17 pages, 1314 KiB  
Article
Parameter Estimation for Composite Dynamical Systems Based on Sequential Order Statistics from Burr Type XII Mixture Distribution
by Tzong-Ru Tsai, Yuhlong Lio, Hua Xin and Hoang Pham
Mathematics 2021, 9(8), 810; https://doi.org/10.3390/math9080810 - 8 Apr 2021
Cited by 1 | Viewed by 1755
Abstract
Considering the impact of the heterogeneous conditions of the mixture baseline distribution on the parameter estimation of a composite dynamical system (CDS), we propose an approach to infer the model parameters and baseline survival function of CDS using the maximum likelihood estimation and [...] Read more.
Considering the impact of the heterogeneous conditions of the mixture baseline distribution on the parameter estimation of a composite dynamical system (CDS), we propose an approach to infer the model parameters and baseline survival function of CDS using the maximum likelihood estimation and Bayesian estimation methods. The power-trend hazard rate function and Burr type XII mixture distribution as the baseline distribution are used to characterize the changes of the residual lifetime distribution of surviving components. The Markov chain Monte Carlo approach via using a new Metropolis–Hastings within the Gibbs sampling algorithm is proposed to overcome the computation complexity when obtaining the Bayes estimates of model parameters. A numerical example is generated from the proposed CDS to analyze the proposed procedure. Monte Carlo simulations are conducted to investigate the performance of the proposed methods, and results show that the proposed Bayesian estimation method outperforms the maximum likelihood estimation method to obtain reliable estimates of the model parameters and baseline survival function in terms of the bias and mean square error. Full article
(This article belongs to the Special Issue Recent Advances of Computational Statistics in Industry and Business)
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9 pages, 278 KiB  
Article
A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria
by Belén Pérez-Sánchez, Martín González, Carmen Perea and Jose J. López-Espín
Mathematics 2021, 9(7), 700; https://doi.org/10.3390/math9070700 - 24 Mar 2021
Cited by 2 | Viewed by 2111
Abstract
Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new [...] Read more.
Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new estimating methods is an important line of research. In fact, if we want to apply the SEM to medical problems with the main goal being to obtain the best approximation between the parameters of model and their estimations. This paper shows a computational study between different methods for estimating simultaneous equations models as well as a new method which allows the estimation of those parameters based on the optimization of the Bayesian Method of Moments and minimizing the Akaike Information Criteria. In addition, an entropy measure has been calculated as a parameter criteria to compare the estimation methods studied. The comparison between those methods is performed through an experimental study using randomly generated models. The experimental study compares the estimations obtained by the different methods as well as the efficiency when comparing solutions by Akaike Information Criteria and Entropy Measure. The study shows that the proposed estimation method offered better approximations and the entropy measured results more efficiently than the rest. Full article
(This article belongs to the Special Issue Recent Advances of Computational Statistics in Industry and Business)
28 pages, 1187 KiB  
Article
GMM Estimation of a Partially Linear Additive Spatial Error Model
by Jianbao Chen and Suli Cheng
Mathematics 2021, 9(6), 622; https://doi.org/10.3390/math9060622 - 15 Mar 2021
Cited by 7 | Viewed by 1648
Abstract
This article presents a partially linear additive spatial error model (PLASEM) specification and its corresponding generalized method of moments (GMM). It also derives consistency and asymptotic normality of estimators for the case with a single nonparametric term and an arbitrary number of nonparametric [...] Read more.
This article presents a partially linear additive spatial error model (PLASEM) specification and its corresponding generalized method of moments (GMM). It also derives consistency and asymptotic normality of estimators for the case with a single nonparametric term and an arbitrary number of nonparametric additive terms under some regular conditions. In addition, the finite sample performance for our estimates is assessed by Monte Carlo simulations. Lastly, the proposed method is illustrated by analyzing Boston housing data. Full article
(This article belongs to the Special Issue Recent Advances of Computational Statistics in Industry and Business)
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18 pages, 1134 KiB  
Article
Incorporating an Asset Health Index into a Life Cycle Costing: A Proposition and Study Case
by Orlando Durán, Fabián Orellana, Pablo Perez and Tamara Hidalgo
Mathematics 2020, 8(10), 1787; https://doi.org/10.3390/math8101787 - 15 Oct 2020
Cited by 5 | Viewed by 2564
Abstract
A physical asset’s health is the consequence of a series of factors, ranging from the characteristics of the location where it operates to the care it is submitted to. These characteristics can influence the durability or the horizon of the useful life of [...] Read more.
A physical asset’s health is the consequence of a series of factors, ranging from the characteristics of the location where it operates to the care it is submitted to. These characteristics can influence the durability or the horizon of the useful life of any equipment, as well as determine its operational performance and its failure rates in the future. Therefore, the assessment of the influence of asset health on Life Cycle Costs is a compelling need. This paper proposes the incorporation of a factor that reflects the projected behavior of an asset’s health index into its corresponding Life Cycle Costing (LCC) model. This allows cost estimates to be made more realistic and LCC models to be operated more accurately. As a way of validating this proposal, a case study is shown. The methodology proposed in this case study was applied in a real case, considering an LNG facility located in central Chile. In addition, sensitivity studies and comparisons with the results obtained by a traditional Life Cycle Costing model are included. The results show the usefulness of incorporating asset health aspects into the Life Cycle Costing of physical assets. Full article
(This article belongs to the Special Issue Recent Advances of Computational Statistics in Industry and Business)
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12 pages, 2278 KiB  
Article
Estimating the COVID-19 Death Toll by Considering the Time-Dependent Effects of Various Pandemic Restrictions
by Hoang Pham
Mathematics 2020, 8(9), 1628; https://doi.org/10.3390/math8091628 - 20 Sep 2020
Cited by 9 | Viewed by 3230
Abstract
COVID-19, known as Coronavirus disease 2019, is caused by a coronavirus called SARS-CoV-2. As coronavirus restrictions ease and cause changes to social and business activities around the world, and in the United States in particular, including social distancing, reopening states, reopening schools, and [...] Read more.
COVID-19, known as Coronavirus disease 2019, is caused by a coronavirus called SARS-CoV-2. As coronavirus restrictions ease and cause changes to social and business activities around the world, and in the United States in particular, including social distancing, reopening states, reopening schools, and the face mask mandates, COVID-19 outbreaks are on the rise in many states across the United States and several other countries around the world. The United States recorded more than 1.9 million new infections in July, which is nearly 36 percent of the more than 5.4 million cases reported nationwide since the pandemic began, including more than 170,000 deaths from the disease, according to data from Johns Hopkins University as of 16 August 2020. In April 2020, the author of this paper presented a model to estimate the number of deaths related to COVID-19, which assumed that there would be no significant change in the COVID-19 restrictions and guidelines in the coming days. This paper, which presents the evolved version of the previous model published in April, discusses a new explicit mathematical model that considers the time-dependent effects of various pandemic restrictions and changes related to COVID-19, such as reopening states, social distancing, reopening schools, and face mask mandates in communities, along with a set of selected indicators, including the COVID-19 recovered cases and daily new cases. We analyzed and compared the modeling results to two recent models based on several model selection criteria. The model could predict the death toll related to the COVID-19 virus in the United States and worldwide based on the data available from Worldometer. The results show the proposed model fit the data significantly better for the United States and worldwide COVID-19 data that were available on 16 August 2020. The results show very encouraging predictability that reflected the time-dependent effects of various pandemic restrictions for the proposed model. The proposed model predicted that the total number of U.S. deaths could reach 208,375 by 1 October 2020, with a possible range of approximately 199,265 to 217,480 deaths based on data available on 16 August 2020. The model also projected that the death toll could reach 233,840 by 1 November 2020, with a possible range of 220,170 to 247,500 American deaths. The modeling result could serve as a baseline to help decision-makers to create a scientific framework to quantify their guidelines related to COVID-19 affairs. The model predicted that the death toll worldwide related to COVID-19 virus could reach 977,625 by 1 October 2020, with a possible range of approximately 910,820 to 1,044,430 deaths worldwide based on data available on 16 August 2020. It also predicted that the global death toll would reach nearly 1,131,000 by 1 November 2020, with a possible range of 1,030,765 to 1,231,175 deaths. The proposed model also predicted that the global death toll could reach 1.47 million deaths worldwide as a result of the SARS CoV-2 coronavirus that causes COVID-19. We plan to apply or refine the proposed model in the near future to further study the COVID-19 death tolls for India and Brazil, where the two countries currently have the second and third highest total COVID-19 cases after the United States. Full article
(This article belongs to the Special Issue Recent Advances of Computational Statistics in Industry and Business)
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20 pages, 422 KiB  
Article
Nonlinear Profile Monitoring Using Spline Functions
by Hua Xin, Wan-Ju Hsieh, Yuhlong Lio and Tzong-Ru Tsai
Mathematics 2020, 8(9), 1588; https://doi.org/10.3390/math8091588 - 15 Sep 2020
Cited by 5 | Viewed by 2149
Abstract
In this study, two new integrated control charts, named T2-MAE chart and MS-MAE chart, are introduced for monitoring the quality of a process when the mathematical form of nonlinear profile model for quality measure is complicated and unable to be specified. [...] Read more.
In this study, two new integrated control charts, named T2-MAE chart and MS-MAE chart, are introduced for monitoring the quality of a process when the mathematical form of nonlinear profile model for quality measure is complicated and unable to be specified. The T2-MAE chart is composed of two memoryless-type control charts and the MS-MAE chart is composed of one memory-type and one memoryless-type control charts. The normality assumption of error terms in the nonlinear profile model for both proposed control charts are extended to a generalized model. An intensive simulation study is conducted to evaluate the performance of the T2-MAE and MS-MAE charts. Simulation results show that the MS-MAE chart outperforms the T2-MAE chart with less false alarms during the Phase I monitoring. Moreover, the MS-MAE chart is sensitive to different shifts on the model parameters and profile shape during the Phase II monitoring. An example about the vertical density profile is used for illustration. Full article
(This article belongs to the Special Issue Recent Advances of Computational Statistics in Industry and Business)
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