• Title/Summary/Keyword: multivariate statistics

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A Bayesian Analysis in Multivariate Bioassay and Multivariate Calibration

  • Park, Nae-Hyun;Lee, Suk-Hoon
    • Journal of the Korean Statistical Society
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    • v.19 no.1
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    • pp.71-79
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    • 1990
  • In the linear model which consider both the multivariate parallel-line bioassay and the multivariate linear calibration, this paper presents a Bayesian procedure which is an extension of Hunter and Lamboy (1981) and has several advantages compared with the non Bayesian techniques. Based on the methods of this article we discuss the effect of multivariate calibration and give a numerical example.

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A Comparison Study of Multivariate Binary and Continuous Outcomes

  • Pak, Dae-Woo;Cho, Hyung-Jun
    • The Korean Journal of Applied Statistics
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    • v.25 no.4
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    • pp.605-612
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    • 2012
  • Multivariate data are often generated with multiple outcomes in various fields. Multiple outcomes could be mixed as continuous and discrete. Because of their complexity, the data are often dealt with by separately applying regression analysis to each outcome even though they are associated the each other. This univariate approach results in the low efficiency of estimates for parameters. We study the efficiency gains of the multivariate approaches relative to the univariate approach with the mixed data that include continuous and binary outcomes. All approaches yield consistent estimates for parameters with complete data. By jointly estimating parameters using multivariate methods, it is generally possible to obtain more accurate estimates for parameters than by a univariate approach. The association between continuous and binary outcomes creates a gap in efficiency between multivariate and univariate approaches. We provide a guidance to analyze the mixed data.

Comparative Study on Statistical Packages for using Multivariate Q-technique

  • Choi, Yong-Seok;Moon, Hee-jung
    • Communications for Statistical Applications and Methods
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    • v.10 no.2
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    • pp.433-443
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    • 2003
  • In this study, we provide a comparison of multivariate Q-techniques in the up-to-date versions of SAS, SPSS, Minitab and S-plus well known to those who study statistics. We can analyze data through the direct Input method(command) in SAS and use of menu method in SPSS, Minitab and S-plus. The analysis performance method is chosen by the high frequency of use. Widely we compare with each Q-techniques form according to input data, input option, statistical chart and statistical output.

ESTIMATING THE SIMULTANEOUS CONFIDENCE LEVELS FOR THE DIFFERENCE OF PROPORTIONS FROM MULTIVARIATE BINOMIAL DISTRIBUTIONS

  • Jeong, Hyeong-Chul;Jhun, Myoung-Shic;Lee, Jae-Won
    • Journal of the Korean Statistical Society
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    • v.36 no.3
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    • pp.397-410
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    • 2007
  • For the two groups data from multivariate binomial distribution, we consider a bootstrap approach to inferring the simultaneous confidence level and its standard error of a collection of the dependent confidence intervals for the difference of proportions with an experimentwise error rate at the a level are presented. The bootstrap method is used to estimate the simultaneous confidence probability for the difference of proportions.

Multivariate Cumulative Sum Control Chart for Dispersion Matrix

  • Chang, Duk-Joon;Shin, Jae-Kyoung
    • Journal of the Korean Data and Information Science Society
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    • v.13 no.2
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    • pp.21-29
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    • 2002
  • Several different control statistics to simultaneously monitor dispersion matrix of several quality variables are presented since different control statistics can be used to describe variability. Multivariare cumulative sum (CUSUM) control charts are proposed and the performances of the proposed CUSUM charts are evaluated in terms of average run length (ARL). Multivariate Shewhart charts are also proposed to compare the properties of the proposed CUSUM charts. The numerical results show that multivariate CUSUM charts are more efficient than multivariate Shewhart charts for small or moderate shifts. And we also found that small reference value of the CUSUM chart is more efficient for small shift.

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Multivariate measures of skewness for the scale mixtures of skew-normal distributions

  • Kim, Hyoung-Moon;Zhao, Jun
    • Communications for Statistical Applications and Methods
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    • v.25 no.2
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    • pp.109-130
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    • 2018
  • Several measures of multivariate skewness for scale mixtures of skew-normal distributions are derived. As a special case, those of multivariate skew-t distribution are considered in detail. Furthermore, the similarities, differences, and behavior of these measures are explored for cases of some specific members of the multivariate skew-normal and skew-t distributions using a simulation study. Since some measures are vectors, it is better to take all measures in the same scale when comparing them. In order to attain such a set of comparable indices, the sample version is considered for each of the skewness measures that are taken as test statistics for the hypothesis of t distribution against skew-t distribution. An application is reported for the data set consisting of 71 total glycerol and magnesium contents in Grignolino wine.

Multivariate volatility for high-frequency financial series (다변량 고빈도 금융시계열의 변동성 분석)

  • Lee, G.J.;Hwang, Sun Young
    • The Korean Journal of Applied Statistics
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    • v.30 no.1
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    • pp.169-180
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    • 2017
  • Multivariate GARCH models are interested in conditional variances (volatilities) as well as conditional correlations between return time series. This paper is concerned with high-frequency multivariate financial time series from which realized volatilities and realized conditional correlations of intra-day returns are calculated. Existing multivariate GARCH models are reviewed comparatively with the realized volatility via canonical correlations and value at risk (VaR). Korean stock prices are analysed for illustration.

Permutation tests for the multivariate data

  • Park, Hyo-Il;Kim, Ju-Sung
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.4
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    • pp.1145-1155
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    • 2007
  • In this paper, we consider the permutation tests for the multivariate data under the two-sample problem setting. We review some testing procedures, which are parametric and nonparametric and compare them with the permutation ones. Then we consider to try to apply the permutation tests to the multivariate data having the continuous and discrete components together by choosing some suitable combining function through the partial testing. Finally we discuss more aspects for the permutation tests as concluding remarks.

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Rank Tests for Multivariate Linear Models in the Presence of Missing Data

  • Lee, Jae-Won;David M. Reboussin
    • Journal of the Korean Statistical Society
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    • v.26 no.3
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    • pp.319-332
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    • 1997
  • The application of multivariate linear rank statistics to data with item nonresponse is considered. Only a modest extension of the complete data techniques is required when the missing data may be thought of as a random sample, and an appropriate modification of the covariances is derived. A proof of the asymptotic multivariate normality is given. A review of some related results in the literature is presented and applications including longitudinal and repeated measures designs are discussed.

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Control Charts for Means and Variances under Multivariate Normal Process

  • Chang, Duk-Joon;Kwon, Yong-Man
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.1
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    • pp.223-232
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    • 1999
  • Multivariate quality control charts with combine-accumulate approach and accumulate-combine apprach for monitoring both means and variances under multivariate normal process are investigated. Numerical performances of the charts show that multivariate EWMA chart with accumulate-combine approach can be recommended for all kinds of shift in means and variances.

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