• Title/Summary/Keyword: Statistical Model

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Modified Nayak's Randomized Response Model

  • Lee, Gi-Sung;Hong, Ki-Hak
    • Communications for Statistical Applications and Methods
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    • v.6 no.1
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    • pp.117-130
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    • 1999
  • Nayak(1994) suggested a combined randomized response model that combined the Warner's model and greenberg et al.'s model. In this paper we extend Nayak's model to two sample case of including unknown unrelated character also propose some combined models such W-M model and G-M model that modify the Nayak's model. We suggest the efficiency conditions of our models for Nayak's model, also find the efficiency condition of G-M model for the W-M model.

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STATIONARY $\beta-MIXING$ FOR SUBDIAGONAL BILINEAR TIME SERIES

  • Lee Oe-Sook
    • Journal of the Korean Statistical Society
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    • v.35 no.1
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    • pp.79-90
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    • 2006
  • We consider the subdiagonal bilinear model and ARMA model with subdiagonal bilinear errors. Sufficient conditions for geometric ergodicity of associated Markov chains are derived by using results on generalized random coefficient autoregressive models and then strict stationarity and ,a-mixing property with exponential decay rates for given processes are obtained.

R and S Arrays Approach for Transfer Function-Noise Model Identificaton

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • v.19 no.1
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    • pp.1-14
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    • 1990
  • This paper proposes an approach to the identification of trnasfer function models. A strategy for the identification of the model structure is based on R and S arrays constructed by the impulse response function of the model. Theoretical patterns of the arrays associated with the model are investigated, and the practical implementation method of the suggested approach is also discussed. Finally two published samples are employed to demonstrate the practicability of the approach.

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Bayesian Outlier Detection in Regression Model

  • Younshik Chung;Kim, Hyungsoon
    • Journal of the Korean Statistical Society
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    • v.28 no.3
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    • pp.311-324
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    • 1999
  • The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for an outlier problem and also analyze it in linear regression model using a Bayesian approach. Then we use the mean-shift model and SSVS(George and McCulloch, 1993)'s idea which is based on the data augmentation method. The advantage of proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability. The MCMC method(Gibbs sampler) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data and a real data.

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A Bayesian Approach to Detecting Outliers Using Variance-Inflation Model

  • Lee, Sangjeen;Chung, Younshik
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.805-814
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    • 2001
  • The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for outliers problem and also analyze it in linear regression model using a Bayesian approach with the variance-inflation model. We will use Geweke's(1996) ideas which is based on the data augmentation method for detecting outliers in linear regression model. The advantage of the proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability The sampling based approach can be used to allow the complicated Bayesian computation. Finally, our proposed methodology is applied to a simulated and a real data.

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Statistical Methods for Tomographic Image Reconstruction in Nuclear Medicine (핵의학 단층영상 재구성을 위한 통계학적 방법)

  • Lee, Soo-Jin
    • Nuclear Medicine and Molecular Imaging
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    • v.42 no.2
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    • pp.118-126
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    • 2008
  • Statistical image reconstruction methods have played an important role in emission computed tomography (ECT) since they accurately model the statistical noise associated with gamma-ray projection data. Although the use of statistical methods in clinical practice in early days was of a difficult problem due to high per-iteration costs and large numbers of iterations, with the development of fast algorithms and dramatically improved speed of computers, it is now inevitably becoming more practical. Some statistical methods are indeed commonly available from nuclear medicine equipment suppliers. In this paper, we first describe a mathematical background for statistical reconstruction methods, which includes assumptions underlying the Poisson statistical model, maximum likelihood and maximum a posteriori approaches, and prior models in the context of a Bayesian framework. We then review a recent progress in developing fast iterative algorithms.

Levels of Statistical Literacy Derived from Middle School Mathematics Textbook (중학교 수학 교과서의 통계적 소양 수준 반영 정도)

  • Choi, Sun Mi;Noh, Jihwa
    • East Asian mathematical journal
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    • v.37 no.4
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    • pp.481-497
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    • 2021
  • The importance of statistics in everyday life and work place has led to calls for an increased attention to statistical literacy in the mathematics curriculum both internationally and domestically. While professional organizations and researchers propose perspectives towards and models of statistical literacy, conceptions and elements of statistical literacy vary. This study examines how mathematics textbook questions fulfill the requirements of statistical literacy by employing two models: Watson's model focusing on understanding of statistical language and Curcio's model on data interpretation aspects of statistical literacy. For this, a total of 872 problem questions presented in the statistics units of from ten textbooks for the middle school year 1 mathematics were analyzed.

A Conditional Randomized Response Model for Detailed Survey

  • Lee, Gi-Sung;Hong, Ki-Hak
    • Communications for Statistical Applications and Methods
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    • v.7 no.3
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    • pp.721-729
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    • 2000
  • In this paper, we propose a new conditional randomized response model that has improved the Carr et al.'s model in view of he variance and the protection of privacy of respondents. We show that he suggested model is more effective and protective than the Loynes' model and Carr et al.' model.

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Road Traffic Noise Simulation for Small-scale Urban Form Alteration Using Spatial Statistical Model (공간통계모형을 이용한 소규모 도시 형태 변경에 따른 소음도 예측)

  • Ryu, Hunjae;Chun, Bum Seok;Park, In Kwon;Chang, Seo Il
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.25 no.4
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    • pp.284-290
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    • 2015
  • Road traffic noise is closely related with urban forms and urban components, such as population, building, traffic and land-use, etc. Hence, it is possible to minimize the noise exposure problem depending on how to plan new town or urban planning alteration. This paper provides ways to apply for urban planning in consideration of noise exposure through road traffic noise estimation for alteration of small-scale urban form. Spatial autoregressive model from the former study is used as statistical model for noise simulation. The simulation results by the spatial statistical model are compared with those by the engineering program-based modeling for 5 scenarios of small-scale urban form alteration. The error from the limitation of containing informations inside the grid cell and the difficulties of reflecting acoustic phenomena exists. Nevertheless, in the stage of preliminary design, the use of the statistical models that have been estimated well could be useful in time and economically.