• 제목/요약/키워드: Multivariate Statistics

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Validity of Blockwise Bootstrapped Empirical Process with Multivariate Stationary Sequences

  • Kim, Tae-Yoon;Shin, Ki-Dong;Song, Gyu-Moon
    • Journal of the Korean Statistical Society
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    • 제30권3호
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    • pp.407-418
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    • 2001
  • Buhlmann(1944) established the validity of the block bootstrap proposed by Kunsch when it is applied to p-dimensional $\alpha$-mixing dependent sequence. But his result requires a rather restrictive condition on p in the sense that p is entangled with dependence structure. We address that such restriction on p(or complication of dependence structure with p) could be removed completely when the underlying dependence structure is replace by more weakly dependent structure such as ø-mixing.

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A Cointegration Test Based on Weighted Symmetric Estimator

  • Son Bu-Il;Shin Key-Il
    • Communications for Statistical Applications and Methods
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    • 제12권3호
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    • pp.797-805
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    • 2005
  • Multivariate unit root tests for the VAR(p) model have been commonly used in time series analysis. Several unit root tests were developed and recently Shin(2004) suggested a cointegration test based on weighted symmetric estimator. In this paper, we suggest a multivariate unit root test statistic based on the weighted symmetric estimator. Using a small simulation study, we compare the powers of the new test statistic with the statistics suggested in Shin(2004) and Fuller(1996).

EWMA Control Charts to Monitor Correlation Coefficients

  • Chang, Duk-Joon;Cho, Gyo-Young;Lee, Jae-Man
    • Communications for Statistical Applications and Methods
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    • 제6권2호
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    • pp.413-422
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    • 1999
  • Multivariate EWMA control charts to simultaneously monitor correlation coefficients of correlated quality characteristics under multivariate normal process are proposed. Performances of the proposed charts are measured in terms of average run length(ARL). Numerical results show that smalle values for smoothing constant with accumulate-combine approach are preferred for detecting smalle shifts.

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A Comparative Study on Bayes Estimators for the Multivariate Normal Mcan

  • Kim, Dal-Ho;Lee, In suk;Kim, Hyun-Sook
    • Communications for Statistical Applications and Methods
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    • 제6권2호
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    • pp.501-510
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    • 1999
  • In this paper, we consider a comparable study on three Bayes procedures for the multivariate normal mean estimation problem. In specific we consider hierarchical Bayes empirical Bayes and robust Bayes estimators for the normal means. Then three procedures are compared in terms of the four comparison criteria(i.e. Average Relative Bias (ARB) Average Squared Relative Bias (ASRB) Average Absolute Bias(AAB) Average Squared Deviation (ASD) using the real data set.

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CUSUM Chart to Monitor Dispersion Matrix for Multivariate Normal Process

  • 장덕준;권용만;홍연웅
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2003년도 춘계학술대회
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    • pp.89-95
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    • 2003
  • Cumulative sum(CUSUM) control charts for monitoring dispersion matrix under multivariate normal process are proposed. Performances of the proposed CUSUM charts are measured in terms of average run length(ARL) by simulation. Numerical results show that small reference values of the proposed CUSUM chart is more efficient for small shifts in the production process.

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다변량 시계열 자료를 이용한 부정맥 예측 (Prediction of arrhythmia using multivariate time series data)

  • 이민혜;노호석
    • 응용통계연구
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    • 제32권5호
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    • pp.671-681
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    • 2019
  • 최근에 부정맥 환자가 증가하면서 머신러닝을 이용한 부정맥을 예측하는 연구가 활발하게 진행되고 있다. 기존의 많은 연구들은 특정한 시점의 RR 간격 데이터에서 추출한 특징변수 다변량 데이터에 기반하여 부정맥을 예측하였다. 본 연구에서는 심장 상태가 시간에 따라 변해가는 패턴도 부정맥 예측에 중요한 정보가 될 수 있다고 생각하여 일정한 시간 간격을 두고 특징변수의 다변량 벡터를 추출하여 쌓음으써 얻어지는 다변량 시계열 데이터로 부정맥을 예측하는 것의 유용성에 대해 살펴보았다. 1-Nearest Neighbor 방법과 그것을 앙상블(ensemble)한 learner를 중심으로 비교했을 경우 시계열의 특징을 고려한 적절한 시계열 거리함수를 선택하여 시계열 정보를 활용한 다변량 시계열 데이터 기반 방법의 분류 성능이 더 좋게 나오는 것을 확인하였다.

Impact of Diverse Configuration in Multivariate Bias Correction Methods on Large-Scale Climate Variable Simulations under Climate Change

  • de Padua, Victor Mikael N.;Ahn Kuk-Hyun
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2023년도 학술발표회
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    • pp.161-161
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    • 2023
  • Bias correction of values is a necessary step in downscaling coarse and systematically biased global climate models for use in local climate change impact studies. In addition to univariate bias correction methods, many multivariate methods which correct multiple variables jointly - each with their own mathematical designs - have been developed recently. While some literature have focused on the inter-comparison of these multivariate bias correction methods, none have focused extensively on the effect of diverse configurations (i.e., different combinations of input variables to be corrected) of climate variables, particularly high-dimensional ones, on the ability of the different methods to remove biases in uni- and multivariate statistics. This study evaluates the impact of three configurations (inter-variable, inter-spatial, and full dimensional dependence configurations) on four state-of-the-art multivariate bias correction methods in a national-scale domain over South Korea using a gridded approach. An inter-comparison framework evaluating the performance of the different combinations of configurations and bias correction methods in adjusting various climate variable statistics was created. Precipitation, maximum, and minimum temperatures were corrected across 306 high-resolution (0.2°) grid cells and were evaluated. Results show improvements in most methods in correcting various statistics when implementing high-dimensional configurations. However, some instabilities were observed, likely tied to the mathematical designs of the methods, informing that some multivariate bias correction methods are incompatible with high-dimensional configurations highlighting the potential for further improvements in the field, as well as the importance of proper selection of the correction method specific to the needs of the user.

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A CENTRAL LIMIT THEOREM FOR THE STATIONARY MULTIVARIATE LINEAR PROCESS GENERATED BY ASSOCIATED RANDOM VICTORS

  • Kim, Tae-Sung;Ko, Mi-Hwa;Chung, Sung-Mo
    • 대한수학회논문집
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    • 제17권1호
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    • pp.95-102
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    • 2002
  • A central limit theorem is obtained for a stationary multivariate linear process of the form (equation omitted), where { $Z_{t}$} is a sequence of strictly stationary m-dimensional associated random vectors with E $Z_{t}$ = O and E∥ $Z_{t}$$^2$ < $\infty$ and { $A_{u}$} is a sequence of coefficient matrices with (equation omitted) and (equation omitted).ted)..ted).).

A SIMPLE VARIANCE ESTIMATOR IN NONPARAMETRIC REGRESSION MODELS WITH MULTIVARIATE PREDICTORS

  • Lee Young-Kyung;Kim Tae-Yoon;Park Byeong-U.
    • Journal of the Korean Statistical Society
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    • 제35권1호
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    • pp.105-114
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    • 2006
  • In this paper we propose a simple and computationally attractive difference-based variance estimator in nonparametric regression models with multivariate predictors. We show that the estimator achieves $n^{-1/2}$ rate of convergence for regression functions with only a first derivative when d, the dimension of the predictor, is less than or equal to 4. When d > 4, the rate turns out to be $n^{-4/(d+4)}$ under the first derivative condition for the regression functions. A numerical study suggests that the proposed estimator has a good finite sample performance.

다변량 GARCH 모형의 CCC 및 ECCC 비교분석 (Extended Constant Conditional Correlation (ECCC) Model for Multivariate GARCH Time Series: an Illustration)

  • 이승연;황선영
    • 응용통계연구
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    • 제27권7호
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    • pp.1219-1228
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    • 2014
  • 다변량 금융시계열 분석모형인 상수조건부상관(CCC)에 대해 알아보았으며, 개개 변동성간의 상호작용을 함께 고려한 확장된 상수조건부상관(ECCC)을 소개하고 국내 금융시계열에 적용하였다. 다양한 이변량 수익률 자료를 통해 CCC와 ECCC를 비교분석하였다.