• Bulletin of the Korean Chemical Society
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• v.34 no.6
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• pp.1845-1847
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• 2013

The lateral photovoltaic effect (LPE) can be observed in semiconductors by irradiating a light spot position between electrodes on sample's surface. Because lateral photovoltaic voltage (LPV) is sensitively changed by light spot position, a LPE device has been tried as a position-sensitive detector. This study discusses the correlation between LPV and conductivity in p-type silicon and nano-structured Au deposited p-type silicon (nano-Au silicon), respectively. Conductivity measurement of the sample was carried out using the four-wire method to eliminate contact resistance, and conductivity dependence on LPV was simultaneously measured by changing the light irradiation position. The result showed a strong correlation between conductivity and LPV in the p-type silicon sample. The correlation coefficient was 0.87. The correlation coefficient between LPV and conductivity for the nano-Au silicon sample was 0.41.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.595-600
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• 1999

We present a simple geometric approach which uses polar transformation and elementary trigonometry to evaluating an orthant probability in a bivariate normal distribution. Figures are provided to illustrate the situation for varying correlation coefficient. We derive the distribution of the sample correlation coefficient from a bivariate normal distribution when the sample size is 2 as an application.

• Journal of the Korean Statistical Society
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• v.29 no.3
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• pp.285-294
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• 2000

In analyzing bivariate date the sample correlation coefficient is often used, and it is quite sensitive to one or few isolated cases. In this article we derive a formula for the effect of $textsc{k}$ observations on the samples correlation coefficient by the deletion method. To give a reference value for the isolated cases the asymptotic distribution fo the formula is derived. Also, we give some interpretations on several types of isolated cases and an example based on a real data set.

• Survey Research
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• v.7 no.2
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• pp.53-69
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• 2006

Weights can be made and imposed in both sample design stage and analysis stage in a sample survey. While in design stage weights are related with sample data acquisition quantities such as sample selection probability and response rate, in analysis stage weights are connected with external quantities, for instance population quantities and some auxiliary information. The final weight is the product of all weights in both stage. In the present paper, we focus on the weight in analysis stage and investigate the effect of such weights imposed on the weighted mean when estimating the population mean. We consider a finite population with a pair of fixed survey value and weight in each unit, and suppose equal selection probability designs. Under the condition we derive the formulas of the bias as well as mean square error of the weighted mean and show that the weighted mean is biased and the direction and amount of the bias can be explained by the correlation between survey variate and weight: if the correlation coefficient is positive, then the weighted mein over-estimates the population mean, on the other hand, if negative, then under-estimates. Also the magnitude of bias is getting larger when the correlation coefficient is getting greater. In addition to theoretical derivation about the weighted mean, we conduct a simulation study to show quantities of the bias and mean square errors numerically. In the simulation, nine weights having correlation coefficient with survey variate from -0.2 to 0.6 are generated and four sample sizes from 100 to 400 are considered and then biases and mean square errors are calculated in each case. As a result, in the case or 400 sample size and 0.55 correlation coefficient, the amount or squared bias of the weighted mean occupies up to 82% among mean square error, which says the weighted mean might be biased very seriously in some cases.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.161-170
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• 2014

It is important to estimate an appropriate quantile for design of hydraulic structure. For this purpose, it is necessary to find the appropriate probability distribution which can represent the sample data well. Probability plot correlation coefficient test as one of goodness-of-fit test, is recently developed and has been known as a simple and powerful method. In this study, probability plot correlation coefficient test statistics using the plotting position considering the coefficients of skewness for the GEV distribution is derived, and represented by the regression equation. Monte-Carlo method is also performed to compare the rejection power between each method. As the results, the probability plot correlation coefficient test which is derived in this study is better than the others. In particular, when sample size is small and distribution has the shape parameter, rejection power of probability plot correlation coefficient test considering the coefficients of skewness is bigger than the others.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.53.1-53.1
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• 2011

We analyze the halo and galaxy catalogs from the Millennium simulations at redshifts z=0, 0.5, 1 to determine the alignment profiles of cluster galaxies in terms of the matter density correlation coefficient and discuss a cosmological implication our result has for breaking parameter degeneracies. For each selected cluster, we measure the alignment between the major axes of the pseudo inertia tensors from all satellites within cluster's virial radius and from only those satellites within some smaller radius. Then we average the measured values over the similar-mass sample to determine the cluster galaxy alignment profile as a function of top-hat scale difference at each redshift. It is shown that the alignment profile of cluster galaxies is well approximated by a power-law of the nonlinear density correlation coefficient that is independent of the power spectrum normalization and bias factor. The alignment profile of cluster galaxies is found to have higher amplitude and lower power-law index when averaged over the larger-mass sample and to have rather weak redshift-dependence. This result is consistent with the picture that the satellite galaxies retain the memory of the external tidal fields right after merging and infalling into the clusters but they gradually lose the initial alignment tendency as the cluster's relaxation proceeds. Demonstrating that the nonlinear density correlation coefficient varies sensitively with the density parameter and neutrino mass fraction, we discuss a potential power of the cluster galaxy alignment profile as an independent probe of cosmology.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.77-91
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• 2022

The optimal threshold estimation is considered in order to discriminate the mixture distribution in the fields of Biostatistics and credit evaluation. There exists well-known various accuracy measures that examine the discriminant power. Recently, Matthews correlation coefficient and the F1 statistic were studied to estimate optimal thresholds. In this study, we explore whether these accuracy measures are appropriate for the optimal threshold to discriminate the mixture distribution. It is found that some accuracy measures that depend on the sample size are not appropriate when two sample sizes are much different. Moreover, an alternative method for finding the optimal threshold is proposed using the correlation coefficient that defines the ratio of the confusion matrix, and the usefulness and utility of this method are also discusses.

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.851-859
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• 2005

Normalized cross-correlation (correlation coefficient) is a useful measure for pattern matching in PIV (Particle Image Velocimetry) analysis. Because it does not have a corresponding simple expression in frequency domain, several fast but inexact measures have been used. Among them, three measures of correlation for PIV analysis and the normalized cross-correlation were evaluated with a sample calculation. The test revealed that all other proposed correlation measures sometimes show inaccurate results, except the normalized cross-correlation. However, correlation coefficient method has a weakpoint that it requires so long time for calculation. To overcome this shortcoming, a fast and exact method for calculating normalized cross-correlation is suggested. It adopts Fast Fourier Transform (FFT) for calculation of covariance and the successive-summing method for the denominator of correlation coefficient. The new algorithm showed that it is really fast and exact in calculating correlation coefficient.

• Journal of Korean Society on Water Environment
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• v.34 no.3
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• pp.251-269
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• 2018

In 1997, the environmental score (Q) corresponding the tolerance value of Korean benthic macroinvertebrate species and the related biotic score, the ecological score of benthic macroinvertebrates (ESB) were proposed; ESB was similar to Beck's biotic index or Chandler's biotic score. This study was carried out to revise the Q values of individual species and the assessment scheme of ESB based on the taxonomic performance and ecological information accumulated since then. The original ESB was renamed as TESB (total ESB), and AESB (average ESB) was newly proposed. AESB is calculated by dividing the TESB (for a given station) by the number of species present in the sample. In this study, TESB showed a positively skewed distribution, while AESB showed a negatively skewed distribution. The correlation between TESB and the concentration of $BOD_5$ was a little stronger than that of the original ESB. TESB showed a very strong correlation (correlation coefficient r = 0.98) with Margalef's species richness, of which correlation coefficient was higher than that of AESB (r = 0.85). AESB showed a strong correlation (r = -0.79) with the concentration of $BOD_5$, while TESB showed a weaker correlation (r = -0.67). Applying TESB and AESB together in an assessment of the environment may be comprehensive because the physical and chemical states of the environment can be evaluated together. AESB is less dependent on the sample size, while TESB tends to increase as the sample size increases. In the evaluation of the environment using TESB, it is necessary to standardize the methods on monitoring.

• Journal of the Korean Statistical Society
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• v.34 no.2
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• pp.125-140
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• 2005

In this paper, we consider the estimation of the correlation coefficient in the bivariate normal distribution, based on a sample obtained using a modification of the moving extreme ranked set sampling technique (MERSS) that was introduced by Al-Saleh and Al-Hadhrami (2003a). The modification involves using a concomitant random variable. Nonparametric-type methods as well as the maximum likelihood estimation are considered under different settings. The obtained estimators are compared to their counterparts that are obtained based simple random sampling (SRS). It appears that the suggested estimators are more efficient