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A-Kappa: A measure of Agreement among Multiple Raters

Shiva Gautam

https://doi.org/10.6339/JDS.201410_12(4).0007

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 12, Issue 4 (2014), pp. 697–716

Abstract

Abstract: Medical data and biomedical studies are often imbalanced with a majority of observations coming from healthy or normal subjects. In the presence of such imbalances, agreement among multiple raters based on Fleiss’ Kappa (FK) produces counterintuitive results. Simulations suggest that the degree of FK’s misrepresentation of the observed agreement may be directly related to the degree of imbalance in the data. We propose a new method for evaluating agreement among multiple raters that is not affected by imbalances, A-Kappa (AK). Performance of AK and FK is compared by simulating various degrees of imbalance and illustrate the use of the proposed method with real data. The proposed index of agreement may provide some insight by relating its magnitude to a probability scale. Existing indices are interpreted arbitrarily. This new method not only provides a measure of overall agreement but also provides an agreement index on an individual item. Computation of both AK and FK may further shed light into the data and be useful in the interpretation and presenting the results.

Estimation of the Inverse Weibull Parameters Under Ranked Set Sampling

Samia A. Salem Mazen Nassar Hanem E. Hozaien

https://doi.org/10.6339/JDS.201910_17(4).0004

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 17, Issue 4 (2019), pp. 696–711

Abstract

In this paper we use the maximum likelihood (ML) and the modified maximum likelihood (MML) methods to estimate the unknown parameters of the inverse Weibull (IW) distribution as well as the corresponding approximate confidence intervals. The estimates of the unknown parameters are obtained based on two sampling schemes, namely, simple random sampling (SRS) and ranked set sampling (RSS). Comparison between the different proposed estimators is made through simulation via their mean square errors (MSE), Pitman nearness probability (PN) and confidence length.

Bivariate Geometric (Maximum) Generalized Exponential Distribution

Debasis Kundu

https://doi.org/10.6339/JDS.201510_13(4).0004

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 13, Issue 4 (2015), pp. 693–712

Abstract

Abstract:In this paper we propose a new five parameter bivariate distribution obtained by taking geometric maximum of generalized exponential distributions. Several properties of this new bivariate distribution and its marginals have been investigated. It is observed that the maximum likelihood estimators of the unknown parameters cannot be obtained in closed form. Five non-linear equations need to be solved simultaneously to compute the maximum likelihood estimators of the unknown parameters. We propose to use the EM algorithm to compute the maximum likelihood estimators of the unknown parameters, and it is computationally quite tractable. We performed extensive simulations study to see the effectiveness of the proposed algorithm, and the performance is quite satisfactory. We analyze one data set for illustrative purposes. Finally we propose some open problems.

The Societal Impact of Economic Anxiety

Gordon G. Bechtel

https://doi.org/10.6339/JDS.2012.10(4).1088

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 10, Issue 4 (2012), pp. 693–710

Abstract

Abstract: Loss of household income and purchasing power are shown to have broad and negative societal effects. The economic anxiety accompanying this loss has its strongest impact on consumer demand, which is the major factor in a nation’s gross domestic product (GDP). Negative effects of economic anxiety are also found on the propensity to vote, political trust, societal satisfaction, and the quality of life. These effects were verified in a cross national sample from the fifth round of the European Social Survey. Simple regression of the true value of consumer demand, etc. on the true value of economic anxiety is made possible by an estimate of the reliability of our economic-anxiety score (cf. Bechtel, 2010; 2011; 2012). This reliability estimate corrects the regression slope of each societal variable for measurement error in the anxiety score.

Marshall-Olkin Log-Logistic Extended Weibull Distribution: Theory, Properties and Applications

Lornah Lepetu Broderick O. Oluyede Boikanyo Makubate All authors (5)

https://doi.org/10.6339/JDS.201710_15(4).00007

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 15, Issue 4 (2017), pp. 691–722

Abstract

Marshall and Olkin (1997) introduced a general method for obtaining more flexible distributions by adding a new parameter to an existing one, called the Marshall-Olkin family of distributions. We introduce a new class of distributions called the Marshall - Olkin Log-Logistic Extended Weibull (MOLLEW) family of distributions. Its mathematical and statistical properties including the quantile function hazard rate functions, moments, conditional moments, moment generating function are presented. Mean deviations, Lorenz and Bonferroni curves, R´enyi entropy and the distribution of the order statistics are given. The Maximum likelihood estimation technique is used to estimate the model parameters and a special distribution called the Marshall-Olkin Log Logistic Weibull (MOLLW) distribution is studied, and its mathematical and statistical properties explored. Applications and usefulness of the proposed distribution is illustrated by real datasets.

Stationary Bootstrap Based Multi-Step Forecasts for Unrestricted VAR Models

U. Beyaztas Abdel-Salam G. Abdel-Salam

https://doi.org/10.6339/JDS.202010_18(4).0006

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 18, Issue 4 (2020), pp. 682–696

Abstract

This paper proposes a new asymptotically valid stationary bootstrap procedure to obtain multivariate forecast densities in unrestricted vector autoregressive models. The proposed method is not based on either backward or forward representations, so it can be used for both Gaussian and non-Gaussian models. Also, it is computationally more efficient compared to the available resampling methods. The finite sample performance of the proposed method is illustrated by extensive Monte Carlo studies as well as a real-data example. Our records reveal that the proposed method is a good competitor or even better than the existing methods based on backward and/or forward representations.

More Powerful Test for Homogeneity of Means Under an Order Restriction in Time Series with Stationary Process

Abouzar Bazyari

https://doi.org/10.6339/JDS.201610_14(4).0006

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 14, Issue 4 (2016), pp. 681–700

Abstract

Abstract: Suppose that an order restriction is imposed among several means in time series. We are interested in testing the homogeneity of these unknown means under this restriction. In the present paper, a test based on the isotonic regression is done for monotonic ordered means in time series with stationary process and short range dependent sequences errors. A test statistic is proposed using the penalized likelihood ratio (PLR) approach. Since the asymptotic null distribution of test statistic is complicated, its critical values are computed by using Monte Carlo simulation method for some values of sample sizes at different significance levels. The power study of our test statistic is provided which is more powerful than that of the test proposed by Brillinger (1989). Finally, to show the application of the proposed test, it is applied to real dataset contains monthly Iran rainfall records.

A New Distribution for Extreme Values: Regression Model, Characterizations and Applications

H.M. Yousof S.M.A. Jahanshahi T.G. Ramires All authors (5)

https://doi.org/10.6339/JDS.201810_16(4).00002

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 16, Issue 4 (2018), pp. 677–706

Abstract

A new four parameter extreme value distribution is defined and studied. Various structural properties of the proposed distribution including ordinary and incomplete moments, generating functions, residual and reversed residual life functions, order statistics are investigated. Some useful characterizations based on two truncated moments as well as based on the reverse hazard function and on certain functions of the random variable are presented. The maximum likelihood method is used to estimate the model parameters. Further, we propose a new extended regression model based on the logarithm of the new distribution. The new distribution is applied to model three real data sets to prove empirically its flexibility.

Weighted Orthogonal Components Regression Analysis

Xiaogang Su Yaa Wonkye Pei Wang All authors (4)

https://doi.org/10.6339/JDS.201910_17(4).0003

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 17, Issue 4 (2019), pp. 674–695

Abstract

In the linear regression setting, we propose a general framework, termed weighted orthogonal components regression (WOCR), which encompasses many known methods as special cases, including ridge regression and principal components regression. WOCR makes use of the monotonicity inherent in orthogonal components to parameterize the weight function. The formulation allows for efficient determination of tuning parameters and hence is computationally advantageous. Moreover, WOCR offers insights for deriving new better variants. Specifically, we advocate assigning weights to components based on their correlations with the response, which may lead to enhanced predictive performance. Both simulated studies and real data examples are provided to assess and illustrate the advantages of the proposed methods.

Sequence Mutations of Genes Pertaining to Malignancy in Cancer

Nardnisa Sintupisut Chen-Hsiang Yeang

https://doi.org/10.6339/JDS.2013.11(4).1121

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 11, Issue 4 (2013), pp. 673–714

Abstract

Abstract: Cancer is a complex disease where various types of molecular aber rations drive the development and progression of malignancies. Among the diverse molecular aberrations, inherited and somatic mutations on DNA se quences are considered as major drivers for oncogenesis. The complexity of somatic alterations is revealed from large-scale investigations of cancer genomes and robust methods for interring the function of genes. In this review, we will describe sequence mutations of several cancer-related genes and discuss their functional implications in cancer. In addition, we will in troduce the on-line resources for accessing and analyzing sequence mutations in cancer. We will also provide an overview of the statistical and computa tional approaches and future prospects to conduct comprehensive analyses of the somatic alterations in cancer genomes.

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Journal of data science

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

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