• Title/Summary/Keyword: Multiple hypothesis

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Objective Bayesian multiple hypothesis testing for the shape parameter of generalized exponential distribution

  • Lee, Woo Dong;Kim, Dal Ho;Kang, Sang Gil
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.1
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    • pp.217-225
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    • 2017
  • This article deals with the problem of multiple hypothesis testing for the shape parameter in the generalized exponential distribution. We propose Bayesian hypothesis testing procedures for multiple hypotheses of the shape parameter with the noninformative prior. The Bayes factor with the noninformative prior is not well defined. The reason is that the most of the noninformative prior can be improper. Therefore we study the default Bayesian multiple hypothesis testing methods using the fractional and intrinsic Bayes factors with the reference priors. Simulation study is performed and an example is given.

Maritime Business Cycles with Multiple Structure Changes

  • Kim, Hyunsok
    • Journal of Navigation and Port Research
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    • v.44 no.5
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    • pp.407-413
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    • 2020
  • In this paper we examined a novel extension of the convergence empirics for the maritime business cycle which considers structure breaks and/or changes. To provide theoretical justification, the convergence hypothesis uses the relaxed assumption to technology shocks. Based on the recent empirical results provided by Kim and Chang (2020), we consider nonlinear dynamics that capture the properties on structural changes in the equilibrium adjustment process. This approach bridges the gap between the theoretical framework and empirical specifications. In particular, we applied the convergence hypothesis to the multiple structure change model for the maritime business cycle. Our application to the maritime data showed support of the convergence hypothesis allowing multiple structure changes during the high volatile period and offers additional insight into the forecasting maritime business cycles.

On-road Vehicle Tracking using Laser Scanner with Multiple Hypothesis Assumption

  • Ryu, Kyung-Jin;Park, Seong-Keun;Hwang, Jae-Pil;Kim, Eun-Tai;Park, Mignon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.9 no.3
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    • pp.232-237
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    • 2009
  • Active safety vehicle devices are getting more attention recently. To prevent traffic accidents, the environment in front and even around the vehicle must be checked and monitored. In the present applications, mainly camera and radar based systems are used as sensing devices. Laser scanner, one of the sensing devices, has the advantage of obtaining accurate measurement of the distance and the geometric information about the objects in the field of view of the laser scanner. However, there is a problem that detecting object occluded by a foreground one is difficult. In this paper, criterions are proposed to manage this problem. Simulation is conducted by vehicle mounted the laser scanner and multiple-hypothesis algorithm tracks the candidate objects. We compare the running times as multi-hypothesis algorithm parameter varies.

Performance Prediction of Multiple Hypothesis Tracking Algorithm (다중 가설 추적 알고리듬의 추적 성능예측)

  • 정영헌
    • Proceedings of the IEEK Conference
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    • 2003.07c
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    • pp.2787-2790
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    • 2003
  • In this paper, we predict tracking performance of the multiple hypothesis tracking (MHT) algorithm. The MHT algorithm is known to be an optimal Bayesian approach and is superior to asly other tracking filters because it takes into account the events that the measurements can be originated from new targets and false alarms 3s well as interesting targets. In the MHT algorithm, a number of candidate hypotheses are generated and evaluated later as more data are received. The probability of each candidate hypotheses is approximately evaluated by using the hybrid conditional average approach (HYCA). We performed numerical experiments to show the validity of our performance prediction.

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MULTIPLE DELETION MEASURES OF TEST STATISTICS IN MULTIVARIATE REGRESSION

  • Jung, Kang-Mo
    • Journal of applied mathematics & informatics
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    • v.26 no.3_4
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    • pp.679-688
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    • 2008
  • In multivariate regression analysis there exist many influence measures on the regression estimates. However it seems to be few of influence diagnostics on test statistics in hypothesis testing. Case-deletion approach is fundamental for investigating influence of observations on estimates or statistics. Tang and Fung (1997) derived single case-deletion of the Wilks' ratio, Lawley-Hotelling trace, Pillai's trace for testing a general linear hypothesis of the regression coefficients in multivariate regression. In this paper we derived more extended form of those measures to deal with joint influence among observations. A numerical example is given to illustrate the effect of joint influence on the test statistics.

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Multivariate Process Control Chart for Controlling the False Discovery Rate

  • Park, Jang-Ho;Jun, Chi-Hyuck
    • Industrial Engineering and Management Systems
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    • v.11 no.4
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    • pp.385-389
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    • 2012
  • With the development of computer storage and the rapidly growing ability to process large amounts of data, the multivariate control charts have received an increasing attention. The existing univariate and multivariate control charts are a single hypothesis testing approach to process mean or variance by using a single statistic plot. This paper proposes a multiple hypothesis approach to developing a new multivariate control scheme. Plotted Hotelling's $T^2$ statistics are used for computing the corresponding p-values and the procedure for controlling the false discovery rate in multiple hypothesis testing is applied to the proposed control scheme. Some numerical simulations were carried out to compare the performance of the proposed control scheme with the ordinary multivariate Shewhart chart in terms of the average run length. The results show that the proposed control scheme outperforms the existing multivariate Shewhart chart for all mean shifts.

A Study of Automatic Multi-Target Detection and Tracking Algorithm using Highest Probability Data Association in a Cluttered Environment (클러터가 존재하는 환경에서의 HPDA를 이용한 다중 표적 자동 탐지 및 추적 알고리듬 연구)

  • Kim, Da-Soul;Song, Taek-Lyul
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.56 no.10
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    • pp.1826-1835
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    • 2007
  • In this paper, we present a new approach for automatic detection and tracking for multiple targets. We combine a highest probability data association(HPDA) algorithm for target detection with a particle filter for multiple target tracking. The proposed approach evaluates the probabilities of one-to-one assignments of measurement-to-track and the measurement with the highest probability is selected to be target- originated, and the measurement is used for probabilistic weight update of particle filtering. The performance of the proposed algorithm for target tracking in clutter is compared with the existing clustering algorithm and the sequential monte carlo method for probability hypothesis density(SMC PHD) algorithm for multi-target detection and tracking. Computer simulation studies demonstrate that the HPDA algorithm is robust in performing automatic detection and tracking for multiple targets even though the environment is hostile in terms of high clutter density and low target detection probability.

Vehicle Tracking using Sequential Monte Carlo Filter (순차적인 몬테카를로 필터를 사용한 차량 추적)

  • Lee, Won-Ju;Yun, Chang-Yong;Kim, Eun-Tae;Park, Min-Yong
    • Proceedings of the KIEE Conference
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    • 2006.10c
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    • pp.434-436
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    • 2006
  • In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be "distracted" causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

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