• Journal of Korean Society for Quality Management
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• 제18권2호
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• pp.127-133
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• 1990

In this article, several problems inherent in histogram estimate of unknown probability density function are discussed. Those include so called sharp comers and bin edge effect. A resolution for these problems occurred with histogram is discussed. The resulting estimate is called kernel density estimate which is most widely used by data analysts. One of the most recent and reliable data-based choices of scale factor (bandwidth) of the estimate, which has been known to be most crucial, is also discussed.

• Communications for Statistical Applications and Methods
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• 제25권3호
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• pp.263-274
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• 2018

One of the common issues in large dataset analyses is to detect and construct homogeneous groups of objects in those datasets. This is typically done by some form of clustering technique. In this study, we present a divisive hierarchical clustering method for two monothetic characteristics of histogram data. Unlike classical data points, a histogram has internal variation of itself as well as location information. However, to find the optimal bipartition, existing divisive monothetic clustering methods for histogram data consider only location information as a monothetic characteristic and they cannot distinguish histograms with the same location but different internal variations. Thus, a divisive clustering method considering both location and internal variation of histograms is proposed in this study. The method has an advantage in interpreting clustering outcomes by providing binary questions for each split. The proposed clustering method is verified through a simulation study and applied to a large U.S. house property value dataset.

• Phonetics and Speech Sciences
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• 제2권3호
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• pp.127-134
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• 2010

Additive noise and channel mismatch strongly degrade the performance of speaker verification systems, as they distort the features of speech. In this paper a histogram transformation technique is presented to improve the robustness of text-independent speaker verification systems. The technique transforms the features extracted from speech such that their histogram is conformed to a reference distribution. The effect of different standard deviations for the reference distribution is investigated. Experimental results indicate that, in channel mismatched environments, the proposed technique offers significant improvements over existing techniques. We also verify performance improvement of the proposed method using statistics.

• Journal of Integrative Natural Science
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• 제13권2호
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• pp.83-86
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• 2020

In In this study, technique has been suggested according to the calculation of thresholds of histogram based on overlap areas for reducing noise while analyzing the functions of them. Suggested algorithm is to convert histogram extracted from color images to gray level and select overlap areas from extracted histogram. In addition, feature table is configured after extracting histogram in the relevant overlap area while comparing and retrieving for query and database video images by using this feature table. Suggested retrieval system has been confirmed to be more outstanding with retrieval function in video images with more noises than the system that only used color histogram.

• School Mathematics
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• 제4권4호
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• pp.677-688
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• 2002

The context and intuitive understanding is very important in Statistics Education. Especially, there is a need to mitigate student's difficulty in studying probability density function. One of teaching method this concept is to using relative frequency histogram. But, as using this method, we should know several problems included in that. This study investigate problems in the method for teaching probability density function as gradual meaning of histogram. Also, as alternative approach, this thesis introduce the density curve concept. The application of four methods to teach the concept of the probability density function and analysis of the survey result is done in this research.

• Journal of the Korean Data and Information Science Society
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• 제20권4호
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• pp.705-712
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• 2009

Histogram is the oldest and most widely used density estimator for presentation and exploration of observed univariate data. The structure of a histogram really depends on the number of bins and the width of the bins, so that slight changes on bins can produce totally different shape of a histogram. In order to solve this problem the fuzzy histogram was introduced and the result was good enough (Loquin and Strauss, 2008). In particular, when estimating loss distribution related with operational risk a histogram has been widely used. In this article, instead of an ordinary histogram we try to use a fuzzy histogram for estimating loss distribution and show that a fuzzy histogram provide more stable results.

• Communications for Statistical Applications and Methods
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• 제20권4호
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• pp.321-327
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• 2013

Bayesian and empirical Bayesian methods have become quite popular in the theory and practice of statistics. However, the objective is to often produce an ensemble of parameter estimates as well as to produce the histogram of the estimates. For example, in insurance pricing, the accurate point estimates of risk for each group is necessary and also proper dispersion estimation should be considered. Well-known Bayes estimates (which is the posterior means under quadratic loss) are underdispersed as an estimate of the histogram of parameters. The adjustment of Bayes estimates to correct this problem is known as constrained Bayes estimators, which are matching the first two empirical moments. In this paper, we propose a way to apply the constrained Bayes estimators in insurance pricing, which is required to estimate accurately both location and dispersion. Also, the benefit of the constrained Bayes estimates will be discussed by analyzing real insurance accident data.

• The Korean Journal of Applied Statistics
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• 제25권1호
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• pp.197-203
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• 2012

In fitting a regression model, we often encounter data sets which do not follow Gaussian distribution and/or do not have equal variance. In this case estimation of the conditional density of a response variable at a given design point is hardly solved by a standard least squares method. To solve this problem, we propose a simple method to estimate the distribution of the fitted vales under heteroscedasticity using the idea of quantile regression and the histogram techniques. Application of this method to a real data sets is given.

• Journal of the Korean Statistical Society
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• 제34권1호
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• pp.49-60
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• 2005

In this paper we introduce a nonparametric method for estimating the probability density function of detection distances in line transect sampling. The estimator is obtained using a frequency histogram density estimation method. The asymptotic properties of the proposed estimator are derived and compared with those of the kernel estimator under the assumption that the data collected satisfy the shoulder condition. We found that the asymptotic mean square error (AMSE) of the two estimators have about the same convergence rate. The formula for the optimal histogram bin width is derived which minimizes AMSE. Moreover, the performances of the corresponding k-nearest-neighbor estimators are studied through simulation techniques. In the absence of our knowledge whether the shoulder condition is valid or not a new semi-parametric model is suggested to fit the line transect data. The performances of the proposed two estimators are studied and compared with some existing nonparametric and semiparametric estimators using simulation techniques. The results demonstrate the superiority of the new estimators in most cases considered.

• MALSORI
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• 제63호
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• pp.153-170
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• 2007

It is well known that when there is an acoustic mismatch between the speech obtained during training and testing, the accuracy of speaker verification systems drastically deteriorates. This paper presents the use of MFCCs' histogram enhancement technique in order to improve the robustness of a speaker verification system. The technique transforms the features extracted from speech within an utterance such that their statistics conform to reference distributions. The reference distributions proposed in this paper are uniform distribution and beta distribution. The transformation modifies the contrast of MFCCs' histogram so that the performance of a speaker verification system is improved both in the clean training and testing environment and in the clean training and noisy testing environment.