• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.367-376
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• 2011

BLS weight adjustment is a widely used method for business surveys with non-responses and outliers. Recent surveys show that the non-response weight adjustment of the BLS method is the same as the ratio imputation method. In this paper, we suggested a modified BLS weight adjustment method by imputing missing values instead of using weight adjustment for non-response. Monthly labor survey data is used for a small Monte-Carlo simulation and we conclude that the suggested method is superior to the original BLS weight adjustment method.

• Korean Journal of Community Nutrition
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• v.17 no.6
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• pp.707-713
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• 2012

The purpose of this study was to verify the difference of middle school students' eating disorder behavior and scholastic adjustment according to the gender and weight group and evaluate correlations between their eating disorder behavior and scholastic adjustment. A survey was conducted in middle school second graders attending five schools located in Seoul. Results from 355 questionnaire sheets were utilized for statistical analysis, and the results were gained as below: With regard to eating disorder behavior by gender, female students showed more eating disorder behavior than male students (p < 0.01); however, scholastic adjustment did not indicate significant differences by gender. According to the result of analyzing scholastic adjustment according to the weight group, the obese group showed lower scholastic adjustment (p < 0.05) and adjustment to studying (p < 0.01) than other groups; however, the eating disorder behavior, attachment to school, and adjustment to school did not indicate significant differences. According to the results of analyzing scholastic adjustment by eating disorder group, the normal group showed higher adjustment to studying than the risky group (p < 0.01). And scholastic adjustment, attachment to school, and adjustment to school did not indicate significant differences. The eating disorder behavior showed negative relationship with adjustment to studying. They showed more eating disorder behavior, their adjustment to studying became lower. Therefore, it is necessary to provide nutritional education at home and at school for them to have appropriate dietary habits as well as health education for them to maintain normal weight.

• Korean Journal of Health Education and Promotion
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• v.23 no.4
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• pp.125-153
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• 2006

This study was intended to describe women's weight controlling by creating a hypothetic model on the weight adjustment behavior and by examining a cause and effect relationship, and to contribute to countermeasures for practicing their promotion of health and improving the quality of life through creating a predictable model. The subject of study was women who utilize the beauty shop located in Seoul, Busan and Daegu and the study period was 12 weeks from July 10 to September 30 in 2004. Gathered 1093 person's general specialty related with weight adjustment and analyzed covariance to prove the hypothesis using statistics compiled from authentic sources. Also proved coincidence of the hypothetical model. Exogenous variables of the hypothetical model are composed of recognition of her body shape, fatness level, age, stress, and self-respect. Endogenous variables are health- control mind, recognized health state, self-efficacy, intention, and behavior of weight adjustment. There were 5 measured variables for exogenous variable(x). There were 8 measured variable(y) for exogenous variable. And coincidence $x^2=297.38$, standard $x^2(x^2/df)=7.08$, GFI=0.962, AGFI=0.917, NFI=0.875, TLI=0.794, CFI=0.889, RMSEA=0.075. The result of hypothesis had an epoch-making record that 20 out of 27 hypothesis was proved positive way. Generally weight adjustment has been highly seen in housewives, the married and the old age. Health control mind seems to be high as fatness level, age, and self-respect are high and low stress. Recognized health state is high as age and self-respect are high and low stress. However, it is not much related with recognition of her body shape and fatness level. If age, self-respect, health control mind, recognized health state and self-efficacy are high intention of behavior is also high, but intention of behavior has no relation with recognition of her body shape, fatness level and stress. If fatness level, age, self-respect, health control mind, recognized health state and self-efficacy and intention of behavior are high, execution of weight adjustment will be high. However, recognized health state and stress has no influence for weight adjustment. To increase the coincidence of hypothesis and take a simple model I modified a model and then I got the coincidence $x^2=215.62$, standard $x^2(x^2/df)=6.34$, GFI=0.970, AGFI=0.931, NFI=0.902, TLI=0.901, CFI=0.915, RMSEA=0.070. This result is a bit better than original hypothetical model's so that this model might be more suitable. In this modification model, the factors of weight adjustment seems to be high according to this order self-efficacy, recognized health state, age, intention, health control mind, self-respect, fatness level and stress. With this result I suggest ; 1. Enforcement of IR that everybody can be controlled weight adjustment herself and continuous education, which is related with regular habit (food, exercise, restriction of a favorite food and behavior training etc.) is also needed. 2. Because self-efficacy is influenced to execution of weight adjustment specific program which can increase self-efficacy should have to develop and we need to utilize it to take care of herself. 3. To protect fatness and be active weight adjustment the peculiar program including the concept of self-respect, recognized health state, health control mind and intention must be developed and not only women but also all of people should be educated. 4. This hypothetical model is forecasting women's weight adjustment behavior and can be utilized for fundamental data to increase those people's health.

• 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.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.843-858
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• 2008

BLS adjustment methods have been able to provide more accurate estimates of total and make samples represent population characteristics by post-adjustment of design weights of samples. However, BLS methods use additional data, for instance number of employee, without this information or using other information, give different weight adjustment factors. In this paper we studied the sensitivity of the variables used in BLS adjustment. The 2007 monthly labor survey data is used in analysis.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.255-256
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• 2007

In case of inappropriate stacking position of the container taking in container yard, the working time for the container would be delayed in taking out because of the occurrence of the re-handle and the increase of the crane moving time. We have to take into account a variety of elements like the crane interference, the container group and stacking height in order to determine the optimal stacking position and decide the weight reflecting the importance of these criteria. We propose the dynamic weight adjustment algorithm for the stacking policy criteria employing the online search in this research.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.635-647
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• 2013

Imputation is a commonly used method to handle missing survey data. The performance of the imputation method is influenced by various factors, especially an outlier. The removal of the outlier in a data set is a simple and effective approach to reduce the effect of an outlier. In this paper in order to improve the precision of multiple imputation, we study a imputation method which reduces the effect of outlier using various weight adjustment methods that include the removal of an outlier method. The regression method in PROC/MI in SAS is used for multiple imputation and the obtained final adjusted weight is used as a weight variable to obtain the imputed values. Simulation studies compared the performance of various weight adjustment methods and Monthly Labor Statistic data is used for real data analysis.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.6_spc
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• pp.736-743
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• 2016

This study describes optimization methods for adjustment of helicopter main rotor tracking and balancing (RTB). RTB is a essential process for helicopter operation and maintenance. Linear and non-linear models were developed with past RTB test results for estimation of RTB adjustment. Then global and sequential optimization methods were applied to the each of models. Utilization of the individual optimization method with each model is hard to fulfill the RTB requirements because of different characteristics of each blade. Therefore an ensemble model was used to integrate every estimated adjustment result, and an adaptive method was also applied to adjustment values of the linear model to update for next estimations. The goal of this developed RTB adjustment optimization program is to achieve the requirements within 2 run. Additional tests for comparison of weight factor of the ensemble model are however necessary.

• Journal of the Korean Statistical Society
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• v.31 no.4
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• pp.469-482
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• 2002

This paper suggests a new method of compensating for wave nonresponse in panel survey, which combines weighting adjustment and imputation. By deleting less frequent nonresponse patterns, we can get simplicity. A new mean estimator under the new combining method is provided and a limited simulation study employing a real data is conducted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3534-3553
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• 2021

The low cost of Bluetooth technology has led to its wide usage in indoor positioning. However, some inherent shortcomings of Bluetooth technology have limited its further development in indoor positioning, such as the unstable positioning state caused by the fluctuation of Received Signal Strength Indicator (RSSI) and the low transmission frequency accompanied by a poor real-time performance in positioning and tracking moving targets. To address these problems, an indoor fusion positioning algorithm of Bluetooth technology and pedestrian dead reckoning (PDR) based on a particle filter with dynamic adjustment of weights calculation strategy (BPDW) will be proposed. First, an orderly statistical filter (OSF) sorts the RSSI values of a period and then eliminates outliers to obtain relatively stable RSSI values. Next, the Group-based Trilateration algorithm (GTP) enhances positioning accuracy. Finally, the particle filter algorithm with dynamic adjustment of weight calculation strategy fuses the results of Bluetooth positing and PDR to improve the performance of positioning moving targets. To evaluate the performance of BPDW, we compared BPDW with other representative indoor positioning algorithms, including fingerprint positioning, trilateral positioning (TP), multilateral positioning (MP), Kalman filter, and strong tracking filter. The results showed that BPDW has the best positioning performance on static and moving targets in simulation and actual scenes.