• Title/Summary/Keyword: Multivariate statistical models

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Residuals Plots for Repeated Measures Data

  • PARK TAESUNG
    • Proceedings of the Korean Statistical Society Conference
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    • 2000.11a
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    • pp.187-191
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    • 2000
  • In the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. In this paper, we propose a simple graphical method to detect outliers and to investigate the goodness of model fit in repeated measures data. The graphical method is based on the quantile-quantile(Q-Q) plots of the $X^2$ distribution and the standard normal distribution. We also propose diagnostic measures to detect influential observations. The proposed method is illustrated using two examples.

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On the Moving Average Models with Multivariate geometric Distributions

  • Baek, Jong-ill
    • Communications for Statistical Applications and Methods
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    • v.6 no.3
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    • pp.677-686
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    • 1999
  • In this paper we introduce a class of moving-average(MA) sequences of multivariate random vectors with geometric marginals. The theory of positive dependence is used to show that in various cases the class of MA sequences consists of associated random variables. We utilize positive dependence properties to obtain weakly probability inequality of the multivariate processes.

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Random Effects Models for Multivariate Survival Data: Hierarchical-Likelihood Approach

  • Ha Il Do;Lee Youngjo;Song Jae-Kee
    • Proceedings of the Korean Statistical Society Conference
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    • 2000.11a
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    • pp.193-200
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    • 2000
  • Modelling the dependence via random effects in censored multivariate survival data has recently received considerable attention in the biomedical literature. The random effects models model not only the conditional survival times but also the conditional hazard rate. Systematic likelihood inference for the models with random effects is possible using Lee and Nelder's (1996) hierarchical-likelihood (h-likelihood). The purpose of this presentation is to introduce Ha et al.'s (2000a,b) inferential methods for the random effects models via the h-likelihood, which provide a conceptually simple, numerically efficient and reliable inferential procedures.

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Volatility Analysis for Multivariate Time Series via Dimension Reduction (차원축소를 통한 다변량 시계열의 변동성 분석 및 응용)

  • Song, Eu-Gine;Choi, Moon-Sun;Hwang, S.Y.
    • Communications for Statistical Applications and Methods
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    • v.15 no.6
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    • pp.825-835
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    • 2008
  • Multivariate GARCH(MGARCH) has been useful in financial studies and econometrics for modeling volatilities and correlations between components of multivariate time series. An obvious drawback lies in that the number of parameters increases rapidly with the number of variables involved. This thesis tries to resolve the problem by using dimension reduction technique. We briefly review both factor models for dimension reduction and the MGARCH models including EWMA (Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model). We create meaningful portfolios obtained after reducing dimension through statistical factor models and fundamental factor models and in turn these portfolios are applied to MGARCH. In addition, we compare portfolios by assessing MSE, MAD(Mean absolute deviation) and VaR(Value at Risk). Various financial time series are analyzed for illustration.

A Comparison of Univariate and Multivariate AR Models for Monthly River Flow Series (월유량에 대한 일변량 및 다변량 AR모형의 비교)

  • 이원환;심재현
    • Water for future
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    • v.23 no.1
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    • pp.99-107
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    • 1990
  • The statistical analysis based on the past hydrologic data required to set up the water resources development plan and design the hydraulic structres rationally. Because hydrologic events have random factors implied, the sotchastic analysis is necessary. In this paper, same order of stochastic models of monthly runoff data(multivariate AR(1) and AR(2) models, univariate AR(1) and AR(2) models) are applied to compare the statistical characteristics. The other purpose of this paper is to compare the monthly series, which is generated by univariate and multivariate models. By comparing and estimating of each simulated series, it is known that the multivariate models, including the time and spatial colinearity, are better in prediction than univariate models in the analysis of monthly flow at south Han river basin.

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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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Comparison of Forecasting Performance in Multivariate Nonstationary Seasonal Time Series Models (다변량 비정상 계절형 시계열모형의 예측력 비교)

  • Seong, Byeong-Chan
    • Communications for Statistical Applications and Methods
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    • v.18 no.1
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    • pp.13-21
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    • 2011
  • This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.

More on directional regression

  • Kim, Kyongwon;Yoo, Jae Keun
    • Communications for Statistical Applications and Methods
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    • v.28 no.5
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    • pp.553-562
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    • 2021
  • Directional regression (DR; Li and Wang, 2007) is well-known as an exhaustive sufficient dimension reduction method, and performs well in complex regression models to have linear and nonlinear trends. However, the extension of DR is not well-done upto date, so we will extend DR to accommodate multivariate regression and large p-small n regression. We propose three versions of DR for multivariate regression and discuss how DR is applicable for the latter regression case. Numerical studies confirm that DR is robust to the number of clusters and the choice of hierarchical-clustering or pooled DR.

Multivariate exponential smoothing models with application to exchange rates (다변량 지수평활모형을 이용한 환율 분석)

  • Lee, Yeonha;Seong, Byeongchan
    • The Korean Journal of Applied Statistics
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    • v.33 no.3
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    • pp.257-267
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    • 2020
  • We introduce multivariate exponential smoothing models based on a vector innovations structural time series framework. The models enable us to exploit potential inter-series dependencies to improve the fit and forecasts of multivariate (vector) time series. Models are applied to forecast the exchange rates of the UK pound (UKP) and US dollar (USD) against the Korean won (KRW) observed on monthly basis; subseqently, we compare their performance with alternative models. We observe that the multivariate exponential smoothing models are superior to alternatives.

Marginal Likelihoods for Bayesian Poisson Regression Models

  • Kim, Hyun-Joong;Balgobin Nandram;Kim, Seong-Jun;Choi, Il-Su;Ahn, Yun-Kee;Kim, Chul-Eung
    • Communications for Statistical Applications and Methods
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    • v.11 no.2
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    • pp.381-397
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    • 2004
  • The marginal likelihood has become an important tool for model selection in Bayesian analysis because it can be used to rank the models. We discuss the marginal likelihood for Poisson regression models that are potentially useful in small area estimation. Computation in these models is intensive and it requires an implementation of Markov chain Monte Carlo (MCMC) methods. Using importance sampling and multivariate density estimation, we demonstrate a computation of the marginal likelihood through an output analysis from an MCMC sampler.