• Korean Journal of Hydrosciences
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• v.6
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• pp.13-22
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• 1995

The augmenting hydrologic data using a correlation procedure has been used to improve the estimates of the mean and variance at the site of interest with short record when one or more near by sites with longer records are available. The variance of the unbiased maximum likelihood estimator of $ derived by Moran based on the multivariate normal distribytion is modified into the form of Matalas and Jacobs for the biveriate normal distribution to get the critical minimum values of the multiple correlation coefficient which give the improvement for estimating the variance at the site of interest. Those values are tabulated for various lengths of short records and the number of sites.

• Journal of the Korean Statistical Society
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• v.7 no.2
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• pp.95-98
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• 1978

This note deals with the estimations of the variance of a normal distribution $N(\theta,c\theta^2)$ where c, the square of coefficient of variation is assumed to be known. This amounts to the estimation of $\theta^2$. The minimum variance estimator among all unbiased estimators linear in $\bar{x}^2$ and $s^2$ where $\bar{x}$ and $s^2$ are the sample mean and variance, respectively, and the minimum risk estimator in the class of all estimators linear in $\bar{x}^2$ and $s^2$ are obtained. It is shown that the suggested estimators are BAN.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.339-351
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• 2017

Resampling approaches were the first techniques employed to compute a variance for the Gini coefficient; however, many authors have shown that an analysis of the Gini coefficient and its corresponding variance can be obtained from a regression model. Despite the simplicity of the regression approach method to compute a standard error for the Gini coefficient, the use of the proposed regression model has been challenging in economics. Therefore in this paper, we focus on a comparative study among the regression approach and resampling techniques. The regression method is shown to overestimate the standard error of the Gini index. The simulations show that the Gini estimator based on the modified regression model is also consistent and asymptotically normal with less divergence from normal distribution than other resampling techniques.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1111-1120
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• 2005

In collaborative filtering systems most users do not rate preferences; so User-Item matrix shows great sparsity because it has missing values for items not rated by users. Generally, the systems predict the preferences of an active user based on the preferences of a group of users. However, default voting methods predict all missing values for all users in User-Item matrix. One of the most common methods predicting default voting values tried two different approaches using the average rating for a user or using the average rating for an item. However, there is a problem that they did not consider the characteristics of items, users, and the distribution of data set. We replace the missing values in the User-Item matrix by the default noting method using user coefficient of variance. We select the threshold of user coefficient of variance by using equations automatically and determine when to shift between the user averages and item averages according to the threshold. However, there are not always regular relations between the averages and the thresholds of user coefficient of variances in datasets. It is caused that the distribution information of user coefficient of variances in datasets affects the threshold of user coefficient of variance as well as their average. We decide the threshold of user coefficient of valiance by combining them. We evaluate our method on MovieLens dataset of user ratings for movies and show that it outperforms previously default voting methods.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.23-32
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• 2012

This paper deals with the bias and variance of regression coefficient estimators in a finite population. We derive approximate formulas for the bias, variance and mean square error of two estimators when we select a fixed-size inclusion probability proportional to the size sample and then estimate regression coefficients by the ordinary least square estimator as well as the weighted least square estimator based on the selected sample data. Necessary and sufficient conditions for the comparison of the two estimators in terms of variance and mean square error are suggested. In addition, a simple example is introduced to numerically compare the variance and mean square error of the two estimators.

• Journal of Pharmaceutical Investigation
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• v.2 no.2
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• pp.18-33
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• 1972

Pharmaceutical Study on the Compressed tablets. Hardness, Friability, Disintegration time and Coefficient of Variance of Compressed tablets. Soo Uck Kim, Sung Hoon seo and Hyun Woo Lee (Department of Pharmaceutics, College of Pharmacy, Kyung Hee University) In order to know Hardness, Friability, Disintegration time and Coefficient of variance of the pharmaceutical tablets the 135 tablets sampled from market were tested in the paper. The samples tested in this paper were as follows: Antipyretics and Analgetics 41 Stomach and Digestives 22 Antituberculars 19 Vitamins 12 Sulfa drugs 9 Others (Antihistaminics etc) 32 Total 135 The results of the investigation are shown in table 1-8, Fig 1-Fig 6. Mean values of Hardness, Friability, Disintegration time and Coefficient of variance in each pharmaceutical preparation are as follows. Antipyretics and Analgetics : Hardness(kg) = 5.83 Antipyretics and Analgetics : Friabil.(%) = 0.82 Antipyretics and Analgetics : Disint.t.(min) = 5.28' Antipyretics and Analgetics : Coeff. of V.(%) = 2.90 Stomach and Digestives : Hardness(kg) = 4.11 Stomach and Digestives : Friabil.(%) = 0.71 Stomach and Digestives : Disint.t.(min) = 3.43' Stomach and Digestives : Coeff. of V.(%) = 2.76 Antituberculars : Hardness(kg) = 4.78 Antituberculars : Friabil.(%) = 0.52 Antituberculars : Disint.t.(min) = 4.32' Antituberculars : Coeff. of V.(%) = 2.99 Vitamins : Hardness(kg) = 1.60 Vitamins : Friabil.(%) = 0.43 Vitamins : Disint.t.(min) = 4.10' Vitamins : Coeff. of V.(%) = 3.19 Sulfa drugs : Hardness(kg) = 4.77 Sulfa drugs : Friabil.(%) = 0.37 Sulfa drugs : Disint.t.(min) = 3.10' Sulfa drugs : Coeff. of V.(%) = 2.09 Others : Hardness(kg) = 2.40 Others : Friabil.(%) = 0.66 Others : Disint.t.(min) = 2.19' Others : Coeff. of V.(%) = 3.10 The following summeries might be shown; 1. Ranges of Hardness, Friability, Disintegration time and Coefficient of variance are respectively 1.6 to 5.38 kg, 0.37 to 0.82%, 2 minut 19 second to 5 minut 28 second and 2.09 to 3.10%. 2. According to the results, it could be indicated that higher Hardness shows lower Friability. 3. Against the general conception between Hardness and Disintegration time, higher Hardness shows lower Disintegration time. 4. It seems that higher mean weight shows lowcr Coefficient variance.

• Journal of Korean Society for Quality Management
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• v.17 no.2
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• pp.101-112
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• 1989

We investigate a procedure which detects the parameter change point in AR(1) by Bayesian Approach using Jeffrey prior, for example, coefficient change point, variance change point, coefficient and variance change point, etc. And we apply our procedure to the simulated data.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.257-265
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• 1996

We consider nonstationary autoregressive autoregressive process with infinite variance of error. In the case of infinite cariance, the limiting distribution of the estimated coefficient is different from that under the finite cariance assumption. In this paper we show that the bootstrap method can be used to approximate the distribution of ordinary least squares estimator of the coefficient in the first order random walk process with infinite variance through some empirical studies and we suggest a test procedure based on bootstrap method for the unit root test.

• Journal of the Korean Data and Information Science Society
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• v.5 no.1
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• pp.59-66
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• 1994

Heller and Simonoff(1990) compared several methods of estimating the regression coefficient in a modified proportional hazards model, when the response variable is subject to censoring. We give another method of estimating the parameters in the model which also allows the dependent variable to be censored and the error distribution to be unspecified. The proposed method differs from that of Miller(1976) and that of Buckely and James(1979). We also obtain the variance estimator of the coefficient estimator and compare that with the Buckely-James Variance estimator studied by Hillis(1993).

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.211-216
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• 2006

Monitoring variability is a vital part of modem statistical process control. The conventional Shewhart Rand S charts address the setting where the in-control process readings have a constant variance. In some settings, however, it is the coefficient of variation, rather than the variance, that should be constant. This paper develops a chart, equivalent to the S chart, for monitoring the coefficient of variation using rational groups of observations.