• Journal of the Korean Data and Information Science Society
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• v.1
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• pp.67-76
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• 1990

For a two-parameter Pareto distribution, the uniformly minimum variance unbiased estimateors(UMVUE) for the function of the two parameters are expressed in terms of confluent hypergeometric function. The variance of the UMVUE is also expressed in terms of hypergeometric function of several variables. UMVUE's for the ${\gamma}th$ moment about zero and several useful parametric functions, and their variances are obtained as special cases. The estimators of Baxter(1980) and Saksena and Johnson(1984) are special cases of our estimator.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.5
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• pp.38-43
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• 1996

Windowing routines have as their purpose the reduction of the sidelobes of a spectral output of the FFT or DFT routines. Windowing routines accomplish this by forcing the beginning and end of any sequence to approach each other in value. Since they must work with any sequence they force the beginning and ending samples near zero. To make up for this reduction in power, windowing routines give extra weight to the values near the middle of the sequence. The difference between windows is the way in which they transition from the low weights near the edges to the higher weights neqr the middle of the sequence. Signal-to-noise ratio(SNR) can be determined by the ratio of the output noisy signal variance to the input noisy signal variance of a window. Standard deviation of noise is reduced by windowing. Thus, the windowing operation improved the SNR of the noisy signal. This paper shows a performance estimation of windowing on a single tone with added Gaussian noise and uniform noise.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.923-932
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• 2014

The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.7
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• pp.925-931
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• 2002

Gibbs sampling algorithms were implemented to the multi-trait threshold animal models with any combinations of multiple binary, ordered categorical, and linear traits and investigate the amount of bias on these models with two kinds of parameterization and algorithms for generating underlying liabilities. Statistical models which included additive genetic and residual effects as random and contemporary group effects as fixed were considered on the models using simulated data. The fully conditional posterior means of heritabilities and genetic (residual) correlations were calculated from 1,000 samples retained every 10th samples after 15,000 samples discarded as "burn-in" period. Under the models considered, several combinations of three traits with binary, multiple ordered categories, and continuous were analyzed. Five replicates were carried out. Estimates for heritabilities and genetic (residual) correlations as the posterior means were unbiased when underlying liabilities for a categorical trait were generated given by underlying liabilities of the other traits and threshold estimates were rescaled. Otherwise, when parameterizing threshold of zero and residual variance of one for binary traits, heritability estimates were inflated 7-10% upward. Genetic correlation estimates were biased upward if positively correlated and downward if negatively correlated when underling liabilities were generated without accounting for correlated traits on prior information. Residual correlation estimates were, consequently, much biased downward if positively correlated and upward if negatively correlated in that case. The more categorical trait had categories, the better mixing rate was shown.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.465-474
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• 1994

This study is concerned with a method which helps human to generate EMG signals accurately and consistently to make reliable design samples of function discriminator for prosthetic arm control. We intend to ensure a signal accuracy and consistency by training human as a signal generation source. For the purposes, we construct a human training system using a digital computer, which generates visual graphes to compare real target motion trajectory with the desired one, to observe EMG signals and their features. To evaluate the effect which affects a feature variance and a feature separability between motion classes by the human training system, we select 4 features such as integral absolute value, zero crossing counts, AR coefficients and LPC cepstrum coefficients. We perform a experiment four times during 2 months. The experimental results show that the hu- man training system is effective for accurate and consistent EMG signal generation and reduction of a feature variance, but is not correlated for a feature separability, The cepstrum coefficient is the most preferable among the used features for reduction of variance, class separability and robustness to a time varing property of EMG signals.

• Korean Journal of Hydrosciences
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• v.3
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• pp.105-113
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• 1992

Various modeling approaches to study a long term behavior of streamflow or groundwater storage have been conducted. In this study, a Multivariate AR (1) Model has been applied to generate monthly flows of the one key station which has historical flows using monthly flows of the three subordinate stations. The Model performance was examined using statistical comparisons between the historical and generated monthly series such as mean, variance, skewness. Also, the correlation coefficients (lag-zero, and lag-one) between the two monthly flows were compared. The results showed that the modeled generated flows were statistically similar to the historical flows.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.246-249
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• 1988

In this paper, a new design technique which uses weighted least-sqare approach for the solution of the pole assignment problem is represented. This technique maybe used to assign some closed loop poles to places which reduce the large system input and output variance due to near pole-zero condition. The least-square approach is also applied to the design of servo self-tuning controller with integrator.

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.235-241
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• 1996

The ordinary least squares estimator of the disturbance variance in the linear regression model with stationary errors is shown to be asymptotically unbiased when the error process has a spectral density bounded from the above and away from zero. Such error processes cover a broad class of stationary processes, including ARMA processes.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.535-540
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• 2006

The relationship between the observable noise model and the satellite elevation angle can be modeled quite well by an exponential function.[Jin, 1996] Noise size and dependence on the elevation angle are, however, different for each observation and receiver type. Therefore, the coefficient determination of this model is an issue, and various methods including PR-CP, single difference, and time difference have been suggested. The limitations of them are difficulty to model the carrier phase noise and to eliminate bias. To overcome these disadvantages for using Jin's model, we suggest zero baseline double difference (DD) and noise sorting algorithm. Data DD technique in zero baseline is useful to eliminate all the troublesome GPS biases, and the remaining error is the sum of GPS measurement noises from two satellites. These DD residuals for hours should be sorted by the combination of satellite elevation angles, and then variance value of the residual for each combination can be estimated. Using these values, we construct an over-determined linear equation whose solution is a set of noise variance for each satellite elevation angle. With 24hr Trimble 4000ssi data, we easily worked out the coefficients of the noise model not only for pseudorange but also for carrier phase. We estimated the standard deviation of the measurement DD using our model, and plotted 1 and 3 sigma lines for every epoch to verify the representation of the residual error. 63.3% of pseudorange residual and 65.9% of phase error did not exceed the 1 sigma lines. Additionally, 99.2% and 99.5% of them lied within 3sigma line. These figures prove that the Gaussian property of measurement noise, and that the suggested model by our algorithm corresponds to the observable noise information.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.529-538
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• 2017

The Ghosh and Kim zero-altered distribution model is used to analyze count data that have too many or too few zeros. The dispersion type parameter ${\delta}$ in the zero-altered distribution model consists of mean, variance and zero probability and has two forms depending on the relation between ${\mu}$ and ${\sigma}^2$. We derived the influence function on ${\delta}$ when ${\sigma}^2{\geq}{\mu}$. To show the validity of the influence function, we used the Census data on the number of births of married women in Korea to compare the estimated changes in ${\delta}$ using this function with those obtained using the direct deletion method. The result proved that the obtained influence function is very accurate in estimating changes in ${\delta}$ when an observation is deleted.