• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.5 s.347
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• pp.128-136
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• 2006

In this paper, design and implement 50W Doherty power amplifiers for 3GPP repeater and base station transceiver system. Efficiency improvement and high power property of ideal Doherty power amplifier is distinguishable; however bias control for implementation of Doherty(GDCHD) amplifier is difficult. To solve the problem, therefore, GDCHD(Gate and Drain Control Hybrid Doherty) power amplifier is embodied to drain bias adjustment circuit to Doherty power amplifier with gate bias adjustment circuit. Experiment result shows that $2.11{\sim}2.17\;GHz$, 3GPP operating frequency band, with 57.03 dB gain, PEP output is 50.30 dBm, W-CDMA average power is 47.01 dBm, and -40.45 dBc ACLR characteristic in 5MHz offset frequency band. Each of the parameter satisfied amplifier specification which we want to design. Especially, GDCHD power amplifier shows proper efficiency performance improvement in uniformity ACLR than Doherty power amplifier.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.375-387
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• 2013

Unit non-response of surveys reduces the efficiency of the estimates and also causes non-response bias especially when there is large difference between respondents and non-respondents. Non-response weighting adjustments have usually been used to compensate for non-response. It is not easy to examine the non-response bias as well as to obtain information on the non-respondents in sample surveys. A household panel survey, called The Survey of Household Finances, was conducted in both 2010 and 2011. In this paper, we assume that non-response households in Wave 2 have strong non-response (non-cooperative) tendency. We classify those households into non-response households in Wave 1. Under this assumption, the characteristics of non-response households, the non-response bias and the effect of non-response adjustments are investigated.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.12
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• pp.1689-1694
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• 2004

This study was conducted to develop a novel adjustment factors for 305 days using 138,103 lactation records and 1,770,764 daily records, which were based on environmental circumstances such as herd year, season, age at calving, dry period and lactating stages. The present study showed that the change of persistency of cows at the first parity from total lactacting characteristics was slowly processed, while it was rapidly changed in cows at the second parity stage. Particularly, there was an outstanding difference between the first and second parity cows. Milk yield and composition increased as the age at calving was increased. In addition, milk yield and composition were higher at the first parity on fall compared with others, and those were higher at the more than second parity on fall and winter compared with other parity stages and seasons. The cow of dry group was included into lactating records of more than second parity stage. The data indicated that optimal results arose from 45-70 days of dry period. Milk yield was decreased when dry period was longer or shorter than 45-70 days. The lactating days were divided into 17, 28 and 38 stages to compare the multiplicative correction factors. The factor was effective at 28 stages on the first parity. The total correlation coefficients were 0.93832, 0.95058 and 0.95076 at the present correction factor, 17 stage and 28 stage, respectively. At second parity, the factor was higher in dry group 1 and 3 at 17 stage, and it was higher in dry group 2 at 28 stage compared with others. Therefore, the present study showed that the percent squared bias (PSB), which was calculated from the novel correction factor, was better than previously used correction factors. Also, the present study indicated that the bias of the novel correction factor was improved, and this factor could be more accurate compared with others.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.375-389
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• 2019

Sample attrition according to a long-term tracking reduces the representativeness of the sample data in a panel study. Most panel surveys in South Korea and other countries have prepared response adjustment weights in order to solve problems regarding representativeness due to sample attrition. In this paper, we divided the panel data into continuous response group and non-continuous response group according to response patterns and considered a weighting adjustment method to reduce the bias of the non-continuous response group. A simulation indicated that the proposed composite estimation type weighting method, which reflected the characteristics of non-continuous response groups, could be more efficient than other weighting methods in terms of reducing non-response bias. As a case study, the proposed methods are applied to the Korean Longitudinal Study of Ageing (KLoSA) data of the Korea Employment Information Service.

• Proceedings of the Korean Association for Survey Research Conference
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• 2005.12a
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• pp.169-183
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• 2005

This paper considers formation of nonresponse weighting adjustment cell for handling unit nonresponse in sample surveys. We propose a multivariate regression tree mehtod for segmentation using the variable of interest and the estimated response probability simultaneously to construct effective nonresponse adjustment cell. One is using only response data and the other is using response and nonresponse data. These two cases are compared in terms of bias.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.631-642
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• 2019

Many methods have been developed to solve problems found in sample surveys involving a large number of item non-responses that cause inaccuracies in estimation. However, the non-response adjustment method used under the assumption of random non-response generates a bias in cases where the response rate is affected by the variable of interest. Chung and Shin (2017) and Min and Shin (2018) proposed a method to improve the accuracy of estimation by appropriately adjusting a bias generated when the response rate is a function of the variables of interest. In this study, we studied a case where the response rate function is linear and the error of the super population model follows normal distribution. We also examined the effect of the number of stratum population on bias adjustment. The performance of the proposed estimator was examined through simulation studies and confirmed through actual data analysis.

• Proceedings of the Korean Magnestics Society Conference
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• 2015.05a
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• pp.149-149
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• 2015

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.103-113
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• 2009

Weighting is a common form of unit nonresponse adjustment in sample surveys where entire questionnaires are missing due to noncontact or refusal to participate. A common approach computes the response weight as the inverse of the response rate within adjustment cells based on covariate information. In this paper, we consider the efficiency and robustness of nonresponse weight adjustment bated on the response propensity and predictive mean. In the simulation study based on 2000 Fishry Census in Korea, the root mean squared errors for assessing the various ways of forming nonresponse adjustment cell s are investigated. The simulation result suggest that the most important feature of variables for inclusion in weighting adjustment is that they are predictive of survey outcomes. Though useful, prediction of the propensity to response is a secondary. Also the result suggest that adjustment cells based on joint classification by the response propensity and predictor of the outcomes is productive.

• Journal of Korea Water Resources Association
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• v.42 no.8
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• pp.659-671
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• 2009

Fuzzy c-means clustering technique is applied to improve the accuracy of G/R ratio used for rainfall estimation by radar reflectivity. G/R ratio is computed by the ground rainfall records at AWS(Automatic Weather System) sites to the radar estimated rainfall from the reflectivity of Kwangduck Mt. radar station with 100km effective range. G/R ratio is calculated by two methods: the first one uses a single G/R ratio for the entire effective range and the other two different G/R ratio for two regions that is formed by clustering analysis, and absolute relative error and root mean squared error are employed for evaluating the accuracy of radar rainfall estimation from two G/R ratios. As a result, the radar rainfall estimated by two different G/R ratio from clustering analysis is more accurate than that by a single G/R ratio for the entire range.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.217-227
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• 2015

In observational study, handling confounders is a primary issue in measuring treatment effect of interest. Historically, a regression with covariate adjustment (covariate-adjusted regression) has been the typical approach to estimate treatment effect incorporating potential confounders into model. However, ever since the introduction of the propensity score, covariate-adjusted regression has been gradually replaced in medical literatures with various balancing methods based on propensity score. On the other hand, there is only a paucity of researches assessing propensity score methods compared with the covariate-adjusted regression. This paper examined the performance of propensity score methods in estimating risk difference and compare their performance with the covariate-adjusted regression by a Monte Carlo study. The study demonstrated in general the covariate-adjusted regression with variable selection procedure outperformed propensity-score-based methods in terms both of bias and MSE, suggesting that the classical regression method needs to be considered, rather than the propensity score methods, if a performance is a primary concern.