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Copula-based Logistic Regression Models for Bivariate Binary Responses

Xiaohu Li Linxiong Li Rui Fang

https://doi.org/10.6339/JDS.201407_12(3).0005

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 12, Issue 3 (2014), pp. 461–476

Abstract

Abstract: The association between bivariate binary responses has been studied using Pearson’s correlation coefficient, odds ratio, and tetrachoric correlation coefficient. This paper introduces a copula to model the association. Numerical comparisons between the proposed method and the existing methods are presented. Results show that these methods are comparative. However, the copula method has a clearer interpretation and is easier to extend to bivariate responses with three or more ordinal categories. In addition, a goodness-of-fit test for the selection of a model is performed. Applications of the method on two real data sets are also presented.

A Quantile Regression Analysis of Family Background Factor Effects on Mathematical Achievement

Maozai Tian

https://doi.org/10.6339/JDS.2006.04(4).283

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 4, Issue 4 (2006), pp. 461–478

Abstract

Abstract: Family background factor can be a very important part of a person’s life. One of the main interests of this paper is to investigate whether the family background factors alter performance on mathematical achievement of the stronger students the same way that weaker students are affected. Using large sample of 2000, 2001 and 2002 mathematics participation in Alberta, Canada, such questions have been investigated by means of quantile regression approach. The findings suggest that there may be differential family-background-factor effects at different points in the conditional distribution of mathematical achievements.

Boiling Points Predictions Study via Dimension Reduction Methods: SIR, PCR and PLSR

Hong Yin Yizeng Liang Qinnan Hu

https://doi.org/10.6339/JDS.2003.01(4).177

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 1, Issue 4 (2003), pp. 461–480

Two-by-two ANOVA: Global and Graphical Comparisons Based on an Extension of the Shift Function

Rand R. Wilcox

https://doi.org/10.6339/JDS.2009.07(4).466

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 7, Issue 4 (2009), pp. 459–468

Abstract

Abstract: When comparing two independent groups, the shift function compares all of the quantiles in a manner that controls the probability of at least one Type I error, assuming random sampling only. Moreover, it provides a much more detailed sense of how groups compare, versus using a single measure of location, and the associated plot of the data can yield valuable insights. This note examines the small-sample properties of an ex tension of the shift function where the goal is to compare the distributions of two specified linear sums of the random variables under study, with an emphasis on a two-by-two design. A very simple method controls the proba bility of a Type I error. Moreover, very little power is lost versus comparing means when sampling is from normal distributions with equal variances.

Choosing the Best Performing Garch Model for Sri Lanka Stock Market by Non-Parametric Specification Test

Aboobacker Jahufer

https://doi.org/10.6339/JDS.201507_13(3).0003

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 13, Issue 3 (2015), pp. 457–472

Abstract

This paper examines the performance of different kind of GARCH models with Gaussian, Student-t and generalized error distribution for Colombo Stock Exchange (CSE), in Sri Lanka. Analyzing the daily closing price index of CSE from January 02, 2007 to March 10, 2013. It was found that the Asymmetric GARCH models give better result than symmetric GARCH model. According to distributional assumption these models under Student-t as well as generalized error provided better fit than normal distributional assumption. The Non-Parametric Specification test suggest that the GARCH, EGARCH, TARCH and APARCH models with Student-t distributional assumption are the most successful model for CSE.

Evaluation of Agreement between Measurement Methods from Data with Matched Repeated Measurements via the Coefficient of Individual Agreement

Michael Haber Jingjing Gao Huiman X Barnhart

https://doi.org/10.6339/JDS.2010.08(3).619

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 8, Issue 3 (2010), pp. 457–469

Abstract

Abstract: We propose a simple method for evaluating agreement between methods of measurement when the measured variable is continuous and the data consists of matched repeated observations made with the same method under different conditions. The conditions may represent different time points, raters, laboratories, treatments, etc. Our approach allows the values of the measured variable and the magnitude of disagreement to vary across the conditions. The coefficient of individual agreement (CIA), which is based on the comparison of the between and within-methods mean squared deviation (MSD) is used to quantify the magnitude of agreement between measurement methods. The new approach is illustrated via two examples from studies designed to compare (a) methods of evaluating carotid stenosis and (b) methods of measuring percent body fat.

Tracking Reproductivity of COVID-19 Epidemic in China with Varying Coefficient SIR Model

Haoxuan Sun Yumou Qiu Han Yan All authors (7)

https://doi.org/10.6339/JDS.202007_18(3).0010

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 18, Issue 3 (2020): Special issue: Data Science in Action in Response to the Outbreak of COVID-19, pp. 455–472

Abstract

We propose a varying coefficient Susceptible-Infected-Removal (vSIR) model that allows changing infection and removal rates for the latest corona virus (COVID-19) outbreak in China. The vSIR model together with proposed estimation procedures allow one to track the reproductivity of the COVID-19 through time and to assess the effectiveness of the control measures implemented since Jan 23 2020 when the city of Wuhan was lockdown followed by an extremely high level of self-isolation in the population. Our study finds that the reproductivity of COVID-19 had been significantly slowed down in the three weeks from January 27th to February 17th with 96.3% and

95.1% reductions in the effective reproduction numbers R among the 30 provinces and 15 Hubei cities, respectively. Predictions to the ending times and the total numbers of infected are made under three scenarios of the removal rates. The paper provides a timely model and associated estimation and prediction methods which may be applied in other countries to track, assess and predict the epidemic of the COVID-19 or other infectious diseases

The Weibull Generalized Flexible Weibull Extension Distribution

Abdelfattah Mustafa Beih S. Desouky Shamsan AL-Garash

https://doi.org/10.6339/JDS.201607_14(3).0004

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 14, Issue 3 (2016), pp. 453–478

Abstract

Abstract: This paper introduces a new four parameters model called the Weibull Generalized Flexible Weibull extension (WGFWE) distribution which exhibits bathtub-shaped hazard rate. Some of it’s statistical properties are obtained including ordinary and incomplete moments, quantile and generating functions, reliability and order statistics. The method of maximum likelihood is used for estimating the model parameters and the observed Fisher’s information matrix is derived. We illustrate the usefulness of the proposed model by applications to real data.

Mosaics for Visualizing Knowledge Structures

Ali Unl¨u Anatol Sargin Martin Viehrig

https://doi.org/10.6339/JDS.2013.11(3).1107

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 11, Issue 3 (2013), pp. 451–478

Abstract

Abstract: Mosaic plots are state-of-the-art graphics for multivariate categor ical data in statistical visualization. Knowledge structures are mathematical models that belong to the theory of knowledge spaces in psychometrics. This paper presents an application of mosaic plots to psychometric data arising from underlying knowledge structure models. In simulation trials and with empirical data, the scope of this graphing method in knowledge space theory is investigated.

An Analysis of Mathematics and Science Achievements of American Youth with Nonparametric Quantile Regression

Maozai Tian Yuan Li Pengpeng Zhou

https://doi.org/10.6339/JDS.2008.06(4).399

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 6, Issue 4 (2008), pp. 449–465

Abstract

Abstract: Considering the importance of science and mathematics achieve ments of young students, one of the most well known observed phenomenon is that the performance of U.S. students in mathematics and sciences is undesirable. In order to deal with the problem of declining mathematics and science scores of American high school students, many strategies have been implemented for several decades. In this paper, we give an in-depth longitudinal study of American youth using a double-kernel approach of non parametric quantile regression. Two of the advantages of this approach are: (1) it guarantees that a Nadaraya-Watson estimator of the conditional func tion is a distribution function while, in some cases, this kind of estimator being neither monotone nor taking values only between 0 and 1; (2) it guar antees that quantile curves which are based on Nadaraya-Watson estimator not absurdly cross each other. Previous work has focused only on mean re gression and parametric quantile regression. We obtained many interesting results in this study.

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Journal of data science

Online ISSN: 1683-8602
Print ISSN: 1680-743X

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