• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.16 no.5
• /
• pp.445-448
• /
• 1991

The two-sample locally optimum rank detection scheme is obtained which uses rank and sign statistics for detection of random signals in additive noise. It is shown that the detector is similar in structure to the locally optimum detector for random signals and to the one-sample locally optimum rank detector for random signals. It is also shown that the detector is a generalization of the two-sample locally optimum rank detector for known signals. In addition , the problem of two-sample locally optimum rank detection of random signals in multiple input case is considered briefly.

• The Korean Journal of Applied Statistics
• /
• v.9 no.2
• /
• pp.145-159
• /
• 1996

We propose an adaptive searching method using some spatial relations among sample points to estimate the interesting area in the spatial population. The fundamental idea is to observe the neighboring sample points when a sample point is satified with some condition of an adaptive searching observation. For obseving the sample points with this method to estimate the area the sample size is decreased. From this result, we may expect to reduce the cost and time consuming in observation the sample points and to draw the shape of the interesting area without prior information of an spatial population. Some analytical simulation results are also presented.

• The Korean Journal of Applied Statistics
• /
• v.28 no.3
• /
• pp.417-427
• /
• 2015

Measures in response rate used to measure the representativeness of the sample (the more high response rate) better explain the representativeness of the sample. However, we cannot often explain the representativeness of the sample because there is nonresponse even in the high response rate. Therefore, Schouten et al. (2009) presented a new R-indicator measure that can be described as a representative of the sample. We research the new estimator of the R-indicator in this paper because there are parameters that require estimations. We describe the meanings as representative of the R-indicator; consequently, the bias and efficiency of the proposed estimator for R-indicator are compared to the existing estimator under various simulations. The representativeness of the sample is also explained by applying the proposed estimators in the actual data.

• The Korean Journal of Applied Statistics
• /
• v.27 no.4
• /
• pp.513-521
• /
• 2014

If the target error of an estimator at the present time is greater than the coefficient of variation(CV) of the estimator at the previous time, sample size at this point should be decreased. Various papers have researched sample size determination methods using the CV of an estimator at the previous time, variation of population size and target error of the estimator at this time in sampling on successive occasions. We research a new sample size determination method additionally using change of population CV. We compare the proposed method with existing ones in various simulation settings.

• The Korean Journal of Applied Statistics
• /
• v.20 no.3
• /
• pp.605-618
• /
• 2007

Adaptive sampling design is the selection procedure which depends on observed values of the variable of interest. It is the method which could be applied to the rare and unapproachable population. Adaptive cluster sampling strategies are more efficient than simple random sampling on equivalent sample size. Adaptive sampling with new estimators through the Rao-blackwell method have lower variance than Horvitz-Thompson (HT) and Hansen-Hurwitz (HH). Also, to determine suitable sample size, it was used expected sample and the method finding appropriate sample size by changing initial sample size were studied.

• The Korean Journal of Applied Statistics
• /
• v.30 no.6
• /
• pp.877-888
• /
• 2017

Spectral analysis is used to determine the frequency of time series data. We first determine the frequency of the series through the power spectrum or the periodogram and then calculate the period of a cycle that may exist in a time series. Estimating the frequency using a Bayesian technique has been developed and proven to be useful; however, the Bayesian estimator for the frequency cannot be analytically solved through mathematical equations and may be handled numerically or computationally. In this paper, we make an inference on the Bayesian frequency through both resampling a parameter by Markov chain Monte Carlo (MCMC) methods and resampling data by bootstrap methods for a time series. We take the Korean real estate price index as an example for Bayesian frequency estimation. We have found a difference in the periods between the sale price index and the long term rental price index, but the difference is not statistically significant.

• The Korean Journal of Applied Statistics
• /
• v.21 no.3
• /
• pp.377-385
• /
• 2008

Most of the sample surveys conducted by several statistics preparation agencies are multipurpose surveys inquiring into several distinguishing items through a single sample. In a multipurpose sample design, the stratification tends to be very complex since the stratification variables which are both multivariate and heterogeneous must be considered collectively. In this paper we point out an outlier effect in a multivariate stratification to which the K-means clustering method is applied and propose to consider outliers prior to the stratification step. We also show an empirical stratification effect under consideration of outliers through a case study of sample design for The Rural Living Indicators.

• The Korean Journal of Applied Statistics
• /
• v.15 no.1
• /
• pp.45-55
• /
• 2002

The 2-level rotation design based on 3 rotation groups is discussed in view of Monthly Retail Trade Survey conducted by the Bureau of Census in U.S., and composite estimators for population characteristics are concerned. The generalized composite estimators and the recursive composite estimators are presented at 2-level rotation design with design gap and variance formulas for the composite estimators are provided. Also under the response variability related with covariance structure and correlation structure from repeated response, relative efficiencies of the composite estimators are compared.

• Proceedings of the Korean Statistical Society Conference
• /
• 2002.11a
• /
• pp.317-320
• /
• 2002

• The Korean Journal of Applied Statistics
• /
• v.28 no.3
• /
• pp.477-483
• /
• 2015

This study deals with the decision problem of sample size by the relative standard error of estimates derived from survey results in successive occasions. The population of the construction in business survey results is used to calculate quartile of the relative standard error of the 1,000 sample obtained from simple or stratified random sampling. The sample size at time t with a relative standard error of the point (t-1) in the successive occasions were calculated according to the sampling method. As a result, in terms of the sample size according to the size of the relative standard error of the (t-1), simple random sampling differs significantly from stratified sampling. In addition, we could see differences in sample size (depending on how the population is stratified) and that careful attention is required in the problem of sample size by the relative standard error of estimates derived from survey results in successive occasions.