• Title/Summary/Keyword: Multi-statistical Methods

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Comparison Study of Multi-class Classification Methods

  • Bae, Wha-Soo;Jeon, Gab-Dong;Seok, Kyung-Ha
    • Communications for Statistical Applications and Methods
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    • v.14 no.2
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    • pp.377-388
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    • 2007
  • As one of multi-class classification methods, ECOC (Error Correcting Output Coding) method is known to have low classification error rate. This paper aims at suggesting effective multi-class classification method (1) by comparing various encoding methods and decoding methods in ECOC method and (2) by comparing ECOC method and direct classification method. Both SVM (Support Vector Machine) and logistic regression model were used as binary classifiers in comparison.

Zooming Statistics: Inference across scales

  • Hannig, Jan;Marron, J.S.;Riedi, R.H.
    • Journal of the Korean Statistical Society
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    • v.30 no.2
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    • pp.327-345
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    • 2001
  • New statistical methods are ended to analyzed data in a multi-scale way. Some multi-scale extensions of stand methods, including novel visualization using dynamic graphics are proposed. These tools are used to explore non-standard structure in internet traffic data.

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Fast Hough Transform Using Multi-statistical Methods (다중 통계기법을 이용한 고속 하프변환)

  • Cho, Bo-Ho;Jung, Sung-Hwan
    • Journal of Korea Multimedia Society
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    • v.19 no.10
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    • pp.1747-1758
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    • 2016
  • In this paper, we propose a new fast Hough transform to improve the processing time and line detection of Hough transform that is widely used in various vision systems. First, for the fast processing time, we reduce the number of features by using multi-statistical methods and also reduce the dimension of angle through six separate directions. Next, for improving the line detection, we effectively detect the lines of various directions by designing the line detection method which detects line in proportion to the number of features in six separate directions. The proposed method was evaluated with previous methods and obtained the excellent results. The processing time was improved in about 20% to 50% and line detection was performed better in various directions than conventional methods with experimental images.

A Study on the Classification of Ultrasonic Liver Images Using Multi Texture Vectors and a Statistical Classifier (다중 거칠기 벡터와 통계적 분류기를 이용한 초음파 간 영상 분류에 관한 연구)

  • 정정원;김동윤
    • Journal of Biomedical Engineering Research
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    • v.17 no.4
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    • pp.433-442
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    • 1996
  • Since one texture property(i.e coarseness, orientation, regularity, granularity) for ultrasound liver ages was not sufficient enough to classify the characteristics of livers, we used multi texture vectors tracted from ultrasound liver images and a statistical classifier. Multi texture vectors are selected among the feature vectors of the normal liver, fat liver and cirrhosis images which have a good separability in those ultrasound liver images. The statistical classifier uses multi texture vectors as input vectors and classifies ultrasound liver images for each multi texture vector by the Bayes decision rule. Then the decision of the liver disease is made by choosing the maximum value from the averages of a posteriori probability for each multi texture vector In our simulation, we obtained higtler correct ratio than that of other methods using single feature vector, for the test set the correct ratio is 94% in the normal liver, 84% in the fat liver and 86% in the cirrhosis liver.

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Generalized Multi-Phase Multivariate Ratio Estimators for Partial Information Case Using Multi-Auxiliary Vatiables

  • Ahmad, Zahoor;Hanif, Muhammad;Ahmad, Munir
    • Communications for Statistical Applications and Methods
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    • v.17 no.5
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    • pp.625-637
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    • 2010
  • In this paper we propose generalized multi-phase multivariate ratio estimators in the presence of multiauxiliary variables for estimating population mean vector of variables of interest. Some special cases have been deduced from the suggested estimator in the form of remarks. The expressions for mean square errors of proposed estimators have also been derived. The suggested estimators are theoretically compared and an empirical study has also been conducted.

Bayesian approach for categorical Table with Nonignorable Nonresponse

  • Choi, Bo-Seung;Park, You-Sung
    • Proceedings of the Korean Statistical Society Conference
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    • 2005.11a
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    • pp.59-65
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    • 2005
  • We propose five Bayesian methods to estimate the cell expectation in an incomplete multi-way categorical table with nonignorable nonresponse mechanism. We study 3 Bayesian methods which were previously applied to one-way categorical tables. We extend them to multi-way tables and, in addition, develop 2 new Bayesian methods for multi-way categorical tables. These five methods are distinguished by different priors on the cell probabilities: two of them have the priors determined only by information of respondents; one has a constant prior; and the remaining two have priors reflecting the difference in the response mechanisms between respondent and non-respondent. We also compare the five Bayesian methods using a categorical data for a prospective study of pregnant women.

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Allocation in Multi-way Stratification by Linear Programing

  • NamKung, Pyong;Choi, Jae-Hyuk
    • Communications for Statistical Applications and Methods
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    • v.13 no.2
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    • pp.327-341
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    • 2006
  • Winkler (1990, 2001), Sitter and Skinner (1994), Wilson and Sitter (2002) present a method which applies linear programing to designing surveys with multi-way stratification, primarily in situation where the desired sample size is less than or only slightly larger than the total number of stratification cells. A comparison is made with existing methods both by illustrating the sampling schemes generated for specific examples, by evaluating sample mean, variance estimation, and mean squared errors, and by simulating sample mean for all methods. The computations required can, however, increase rapidly as the number of cells in the multi-way classification increase. In this article their approach is applied to multi-way stratification using real data.

Techniques of Selection of Landslide Hazard Area on Analysis of Topographical Data (지형 데이타 해석에 따른 산사태 위험지역 선정 기법)

  • 강인준;최철웅;장용구
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.12 no.2
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    • pp.147-154
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    • 1994
  • Landslides in the close residential area occur loss of life and properties. Usually soil mechanical methods and multi-statistical methods used to analysis the landslide. The factors of landslide analysis by multi-statistical methods are composite operations of geology, topography, vegetation, and climate etc. In this study, existing landslide areas are cheesed to extract the characteristics of their geographical data for adaption of Multi-statical methods. Authors give a weight to their values and analyze the some area using technique of overlap on GSIS.

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Higber Order Expansions of the Cumulants and the Modified Normalizing Process of Multi-dimensional Maximum Likelihood Estimator

  • Jonghwa Na
    • Communications for Statistical Applications and Methods
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    • v.6 no.1
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    • pp.305-318
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    • 1999
  • In this paper we derive the higher order expansions of the first four cumulants of multi-dimensional Maximum Likelihood Estimator (MLE) under the general parametric model up to and including terms of order O({{{{ {n }^{-1 } }}}}) Also we obtain the explicit form of the expansion of the normalizing trans formation of multi-dimensional MLE and show that the suggested normalizing process is much better than the normal approximation based on central limit theorem through example.

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Estimation of Mean Using Multi Auxiliary Information in Presence of Non Response

  • Kumar, Sunil;Singh, Housila P.
    • Communications for Statistical Applications and Methods
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    • v.17 no.3
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    • pp.391-411
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    • 2010
  • For estimating the mean of a finite population, three classes of estimators using multi-auxiliary information with unknown means using two phase sampling in presence of non-response have been proposed with their properties. Asymptotically optimum estimator(AOE) in each class has been identified along with their mean squared error formulae. An empirical study is also given.