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Journal of Advanced Statistics

Number 2, 2018

7
A Modified Method Using the Bethe Hessian Matrix to Estimate the Number of Communities

Authors: Laala Zeyneb

Abstract

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Number of views: 493

The community detection is one of the main problems in social network analysis. Many methods are proposed to recover the community labels of nodes by assuming the number of communities is known. There has been an increasing interest to explore the communities number. We propose a fast method based on spectral proprieties of the graph to estimate the number K of groups. The method performs well, especially when the number of groups in network is large, and we focus on when the network is unbalanced.

Number 1, 2018

13
Study of the Nonparametric Kaplan-Meier Estimator of the Cumulative Incidence Function in Competiting Risks

Authors: Didier Alain Njamen Njomen

Abstract

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Number of views: 374

In this paper, inspired by the estimator of the cumulated specific incidence proposed by Marubini and Vasecchi [23], we obtain the Kaplan-Meier estimator of the survival function of all causes of death combined by summing the estimator of fj(t) (j 2 {1, . . . ,m}) obtained by plug-in. We establish that the Kaplan-Meier estimator of the survival function overestimates the cumulative incidence in the presence of competitive events. By making the product of all the contributions of the studied system, we establish the likelihood function of the specific risk function for the competitive risk model. Finally, under the assumptions of Dinse and Larson [13], and using the delta-method, we establish the variance of the cumulative incidence function in competiting risks.

Number 4, 2017

12
A Discriminant Function Analysis Approach to Country’s Economy Status

Authors: Egbo M.N, Bartholomew D.C

Abstract

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Number of views: 1119

In this paper, discriminant function analysis was used to classify 68 randomly selected countries to their respective economy status (Weak or Strong) on World Bank website data. The following economic indicators were used as independent variables: Gross Domestic Product (GDP), Ln(Mortality_rate), Ln(Inflation_rate) and Access_to_electricicty. It showed that Ln(Mortality_rate) and Access_to_electricity contributed most to linearly discriminate within the group (economy status) for the two levels (weak or strong) while GDP contributed least with 93.6% sensitivity and 91.2% of cross-validated grouped cases correctly classified.

Number 3, 2017

8
A Note on Dirichlet Process based Semiparametric Bayesian Models

Authors: Arpita Chatterjee

Abstract

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Number of views: 349

Parametric models have been the dominant paradigm for Bayesian inferential works. This is mainly due to its simplicity and straightforward computations. However, given recent computational advances, Semiparametric Bayesian models have become increasingly popular to fit models under flexible distributional assumption. Dirichlet process mixture models form a particular class of Bayesian semiparametric models by assuming a random mixing distribution, taken to be a realization from a Dirichlet process. In this research, we show that even though hierarchical DP models provide flexibility in model fit, they may not perform uniformly better in other aspects as compared to the parametric models. If the DP model gives a better fit, then it should be used regardless of any effect it might have on the power. However, if it results in a reduction in power, then that is just the price of doing a good statistical analysis.

13
The Concept of Majorization in Experimental Designs

Authors: Miltiadis S. Chalikias

Abstract

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Number of views: 415

The information of interest is contained in the variance matrix, V = σ 2Q−1 , or equivalently in the information matrix Q . The problem of finding estimators minimizing a decreasing convex functional of the variance matrix or maximizing an increasing concave functional of the information matrix is crucial in statistics. There are several optimality criteria in bibliography. We explain how the concept of Majorization can be used to investigate optimal experimental designs.

Number 2, 2017

7
Improved Estimator of Measure for Marginal Homogeneity using Marginal Odds in Square Contingency Tables

Authors: Kiyotaka Iki, Sadao Tomizawa

Abstract

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Number of views: 425

For square contingency tables, Iki, Tahata and Tomizawa (2011) considered the measure to represent the degree of departure from the marginal homogeneity model. Using the first-order term in the Taylor series expansion, the estimated measure with the cell probabilities replaced by the corresponding sample proportions is an approximately unbiased estimator when the sample size is large. The present paper proposes the improved approximate unbiased estimator of the measure which is obtained by using the second-order term in the Taylor series expansion. Also, it shows that the improved estimator approaches to the true measure faster than the original estimator as the sample size becomes larger by the simulation studies.

13
Weighted Meta-Analysis for Small Sample Microarray Studies

Authors: Dallas Joder, Nusrat Jahan

Abstract

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Number of views: 439

An abundance of data from microarray studies are available in publicly-accessible databases. Most of these studies are conducted by university based research labs. It is not uncommon for such studies to run only three or four replicates for each experimental condition tested. With this low sample size and the high variability and multiple testing problems inherent to microarray technology, it is difficult to draw statistically significant conclusions from any one such study. Meta-analysis could improve this situation by combining evidence from related studies to increase statistical power. In this work we discussed several meta-analysis methods for small sample gene expression studies. We compared the performances of the traditional Fisher’s log-sum and Stouffer’s- Z meta-analysis methods, as well as three weighted variants of Stouffer’s method. Higher false discovery rates were observed for the traditional methods compared to the weighted methods.

18
Network Exploration by Complements of Graphs with Graph Coloring

Authors: Tai-Chi Wang, Frederick Kin Hing Phoa, Yuan-Lung Lin

Abstract

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Number of views: 662

Network data have become very popular with the growth of technologies and social applications such as Twitter and Facebook. However, few visualization tools have been created for exploring large-scale networks. We propose a simple and quick procedure to explore a network in this study. The algorithm changes the edge representation based on the complement of a simple graph and the partition method of vertex coloring. Furthermore, the colors provide additional information on top of the partitions. Our proposed method is demonstrated in some famous networks.

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