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한국통계학회> CSAM(Communications for Statistical Applications and Methods)> Ranking subjects based on paired compositional data with application to age-related hearing loss subtyping

KCI등재

Ranking subjects based on paired compositional data with application to age-related hearing loss subtyping

Jin Hyun Nam , Aastha Khatiwada , Lois J. Matthews , Bradley A. Schulte , Judy R. Dubno , Dongjun Chung
  • : 한국통계학회
  • : CSAM(Communications for Statistical Applications and Methods) 27권2호
  • : 연속간행물
  • : 2020년 03월
  • : 225-239(15pages)

DOI


목차

1. Introduction
2. Material and methods
3. Results
4. Conclusion
Competing interests
Acknowledgement
References

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초록 보기


						
Analysis approaches for single compositional data are well established; however, effective analysis strategies for paired compositional data remain to be investigated. The current project was motivated by studies of age-related hearing loss (presbyacusis), where subjects are classified into four audiometric phenotypes that need to be ranked within these phenotypes based on their paired compositional data. We address this challenge by formulating this problem as a classification problem and integrating a penalized multinomial logistic regression model with compositional data analysis approaches. We utilize Elastic Net for a penalty function, while considering average, absolute difference, and perturbation operators for compositional data. We applied the proposed approach to the presbyacusis study of 532 subjects with probabilities that each ear of a subject belongs to each of four presbyacusis subtypes. We further investigated the ranking of presbyacusis subjects using the proposed approach based on previous literature. The data analysis results indicate that the proposed approach is effective for ranking subjects based on paired compositional data.

UCI(KEPA)

간행물정보

  • : 자연과학분야  > 통계학
  • : KCI등재
  • :
  • : 격월
  • : 2287-7843
  • :
  • : 학술지
  • : 연속간행물
  • : 1994-2020
  • : 1908


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1A class of CUSUM tests using empirical distributions for tail changes in weakly dependent processes

저자 : Junhyeong Kim , Eunju Hwang

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 27권 2호 발행 연도 : 2020 페이지 : pp. 163-175 (13 pages)

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We consider a wide class of general weakly-dependent processes, called ψ-weak dependence, which unify almost all weak dependence structures of interest found in statistics under natural conditions on process parameters, such as mixing, association, Bernoulli shifts, and Markovian sequences. For detecting the tail behavior of the weakly dependent processes, change point tests are developed by means of cumulative sum (CUSUM) statistics with the empirical distribution functions of sample extremes. The null limiting distribution is established as a Brownian bridge. Its proof is based on the ψ-weak dependence structure and the existence of the phantom distribution function of stationary weakly-dependent processes. A Monte-Carlo study is conducted to see the performance of sizes and powers of the CUSUM tests in GARCH(1, 1) models; in addition, real data applications are given with log-returns of financial data such as the Korean stock price index

2Forecasting evaluation via parametric bootstrap for threshold-INARCH models

저자 : Deok Ryun Kim , Sun Young Hwang

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 27권 2호 발행 연도 : 2020 페이지 : pp. 177-187 (11 pages)

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This article is concerned with the issue of forecasting and evaluation of threshold-asymmetric volatility models for time series of count data. In particular, threshold integer-valued models with conditional Poisson and conditional negative binomial distributions are highlighted. Based on the parametric bootstrap method, some evaluation measures are discussed in terms of one-step ahead forecasting. A parametric bootstrap procedure is explained from which directional measure, magnitude measure and expected cost of misclassification are discussed to evaluate competing models. The cholera data in Bangladesh from 1988 to 2016 is analyzed as a real application.

3Bayesian inference for an ordered multiple linear regression with skew normal errors

저자 : Jeongmun Jeong , Younshik Chung

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 27권 2호 발행 연도 : 2020 페이지 : pp. 189-199 (11 pages)

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This paper studies a Bayesian ordered multiple linear regression model with skew normal error. It is reasonable that the kind of inherent information available in an applied regression requires some constraints on the coefficients to be estimated. In addition, the assumption of normality of the errors is sometimes not appropriate in the real data. Therefore, to explain such situations more flexibly, we use the skew-normal distribution given by Sahu et al. (The Canadian Journal of Statistics, 31, 129-150, 2003) for error-terms including normal distribution. For Bayesian methodology, the Markov chain Monte Carlo method is employed to resolve complicated integration problems. Also, under the improper priors, the propriety of the associated posterior density is shown. Our Bayesian proposed model is applied to NZAPB's apple data. For model comparison between the skew normal error model and the normal error model, we use the Bayes factor and deviance information criterion given by Spiegelhalter et al. (Journal of the Royal Statistical Society Series B (Statistical Methodology), 64, 583-639, 2002). We also consider the problem of detecting an influential point concerning skewness using Bayes factors. Finally, concluding remarks are discussed.

4Bayesian baseline-category logit random effects models for longitudinal nominal data

저자 : Jiyeong Kim , Keunbaik Lee

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 27권 2호 발행 연도 : 2020 페이지 : pp. 201-210 (10 pages)

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Baseline-category logit random effects models have been used to analyze longitudinal nominal data. The models account for subject-specific variations using random effects. However, the random effects covariance matrix in the models needs to explain subject-specific variations as well as serial correlations for nominal outcomes. In order to satisfy them, the covariance matrix must be heterogeneous and high-dimensional. However, it is difficult to estimate the random effects covariance matrix due to its high dimensionality and positive-definiteness. In this paper, we exploit the modified Cholesky decomposition to estimate the high-dimensional heterogeneous random effects covariance matrix. Bayesian methodology is proposed to estimate parameters of interest. The proposed methods are illustrated with real data from the McKinney Homeless Research Project.

5Positive and negative predictive values by the TOC curve

저자 : Chong Sun Hong , So Yeon Choi

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 27권 2호 발행 연도 : 2020 페이지 : pp. 211-224 (14 pages)

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Sensitivity and specificity are popular measures described by the receiver operating characteristic (ROC) curve. There are also two other measures such as the positive predictive value (PPV) and negative predictive value (NPV); however, the PPV and NPV cannot be represented by the ROC curve. Based on the total operating characteristic (TOC) curve suggested by Pontius and Si (International Journal of Geographical Information Science, 97, 570-583, 2014), explanatory methods are proposed to geometrically describe the PPV and NPV by the TOC curve. It is found that the PPV can be regarded as the slope of the right-angled triangle connecting the origin to a certain point on the TOC curve, while 1 - NPV can be represented as the slope of the right-angled triangle connecting a certain point to the top right corner of the TOC curve. When the neutral zone exists, the PPV and 1-NPV can be described as the slopes of two other right-angled triangles of the TOC curve. Therefore, both the PPV and NPV can be estimated using the TOC curve, whether or not the neutral zone is present.

6Ranking subjects based on paired compositional data with application to age-related hearing loss subtyping

저자 : Jin Hyun Nam , Aastha Khatiwada , Lois J. Matthews , Bradley A. Schulte , Judy R. Dubno , Dongjun Chung

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 27권 2호 발행 연도 : 2020 페이지 : pp. 225-239 (15 pages)

다운로드

(기관인증 필요)

초록보기

Analysis approaches for single compositional data are well established; however, effective analysis strategies for paired compositional data remain to be investigated. The current project was motivated by studies of age-related hearing loss (presbyacusis), where subjects are classified into four audiometric phenotypes that need to be ranked within these phenotypes based on their paired compositional data. We address this challenge by formulating this problem as a classification problem and integrating a penalized multinomial logistic regression model with compositional data analysis approaches. We utilize Elastic Net for a penalty function, while considering average, absolute difference, and perturbation operators for compositional data. We applied the proposed approach to the presbyacusis study of 532 subjects with probabilities that each ear of a subject belongs to each of four presbyacusis subtypes. We further investigated the ranking of presbyacusis subjects using the proposed approach based on previous literature. The data analysis results indicate that the proposed approach is effective for ranking subjects based on paired compositional data.

7Joint latent class analysis for longitudinal data: an application on adolescent emotional well-being

저자 : Eun Ah Kim , Hwan Chung , Saebom Jeon

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 27권 2호 발행 연도 : 2020 페이지 : pp. 241-254 (14 pages)

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This study proposes generalized models of joint latent class analysis (JLCA) for longitudinal data in two approaches, a JLCA with latent profile (JLCPA) and a JLCA with latent transition (JLTA). Our models reflect cross-sectional as well as longitudinal dependence among multiple latent classes and track multiple class-sequences over time. For the identifiability and meaningful inference, EM algorithm produces maximum-likelihood estimates under local independence assumptions. As an empirical analysis, we apply our models to track the joint patterns of adolescent depression and anxiety among US adolescents and show that both JLCPA and JLTA identify three adolescent emotional well-being subgroups. In addition, JLCPA classifies two representative profiles for these emotional well-being subgroups across time, and these profiles have different tendencies according to the parent-adolescent-relationship subgroups.

8A fast approximate fitting for mixture of multivariate skew t-distribution via EM algorithm

저자 : Seung-gu Kim

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 27권 2호 발행 연도 : 2020 페이지 : pp. 255-268 (14 pages)

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A mixture of multivariate canonical fundamental skew t-distribution (CFUST) has been of interest in various fields. In particular, interest in the unsupervised learning society is noteworthy. However, fitting the model via EM algorithm suffers from significant processing time. The main cause is due to the calculation of many multivariate t-cdfs (cumulative distribution functions) in E-step. In this article, we provide an approximate, but fast calculation method for the in univariate fashion, which is the product of successively conditional univariate t-cdfs with Taylor's first order approximation. By replacing all multivariate t-cdfs in E-step with the proposed approximate versions, we obtain the admissible results of fitting the model, where it gives 85% reduction time for the 5 dimensional skewness case of the Australian Institution Sport data set. For this approach, discussions about rough properties, advantages and limits are also presented.

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