• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.262-264
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• 2005

We propose a filter design method to suppress the effect of gyroscope mixed random errors at INS system level. It is based on the result that mixed random errors can be represented by a single equivalent ARMA model. At first step, the time difference of equivalent ARMA process is performed, which consider the characteristic of indirect feedback Kalman filter used in INS filter. Next, a state space conversion of time differenced ARMA model is achieved. If the order of AR is greater than that of MA, the controllable or observable canonical form is used. Otherwise, we introduce the state equation of which the state variable is composed of the ARMA model output and several step ahead predicts of that. At final step, a complete form state equation is presented. The simulation results shows that the proposed method gives less transient error and better convergence compared to the conventional filter which assume the mixed random errors as white noise.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.4
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• pp.47-52
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• 2006

Using the equivalent ARMA model representation of the mixed random errors, we propose Klaman filter design methods for aided INS(Inertial Navigation System) which contains the gyroscope mixed random errors. At first step, considering the characteristic of indirect feedback Kalman filter used in the aided INS, we perform the time difference of equivalent ARMA model. Next, according to the order of the time differenced ARMA model, we achieve the state space conversion of that by two methods. If the order of AR part is greater than MA part, we use controllable or observable canonical form. Otherwise, we establish the state apace equation via the method that several step ahead predicts are included in the state variable, where we can derive high and low order models depending on the variable which is compensated from gyroscope output. At final step, we include the state space equation of gyroscope mixed random errors into aided INS Kalman filter model. Through the simulation, we show that both the high and low order filter models proposed give less navigation errors compared to the conventional filter which assume the mixed random errors as white noise.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.225-234
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• 1999

We consider the following semi-parametric non-linear mixed effect regression model : y\ulcorner=f($\chi$\ulcorner;$\beta$)+$\sigma$$\mu$($\chi$\ulcorner)+$\sigma$$\varepsilon$\ulcorner,i=1,…,n,y*=f($\chi$;$\beta$)+$\sigma$$\mu$($\chi$) where y'=(y\ulcorner,…,y\ulcorner) is a vector of n observations, y* is an unobserved new random variable of interest, f($\chi$;$\beta$) represents fixed effect of known functional form containing unknown parameter vector $\beta$\ulcorner=($\beta$$_1$,…,$\beta$\ulcorner), $\mu$($\chi$) is a random function of mean zero and the known covariance function r(.,.), $\varepsilon$'=($\varepsilon$$_1$,…,$\varepsilon$\ulcorner) is the set of uncorrelated measurement errors with zero mean and unit variance and $\sigma$ is an unknown dispersion(scale) parameter. On the basis of finite-sample, small-dispersion asymptotic framework, we derive an absolute lower bound for the asymptotic mean squared errors of prediction(AMSEP) of the regular-consistent non-linear predictors of the new random variable of interest y*. Then we construct an optimal predictor of y* which attains the lower bound irrespective of types of distributions of random effect $\mu$(.) and measurement errors $\varepsilon$.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.69.2-69
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• 2002

Process measurements contain random and gross errors and the size estimation of gross errors is required for production accounting. Mixed integer programming technique had been applied to identify and estimate the gross errors simultaneously. However, the compensate model based on mixed integer programming used all measured variables or spanning tree as gross error candidates. This makes gross error estimation problem combinatorial or computationally expensive. Mixed integer programming with test statistics is proposed for computationally inexpensive gross error identification /estimation. The gross error candidates are identified by measurement test and the set of gross error candidates are...

• Communications of the Korean Mathematical Society
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• v.28 no.2
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• pp.243-267
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• 2013

This paper is dedicated to study a new class of general nonlinear random A-maximal $m$-relaxed ${\eta}$-accretive (so called (A, ${\eta}$)-accretive [49]) equations with random relaxed cocoercive mappings and random fuzzy mappings in $q$-uniformly smooth Banach spaces. By utilizing the resolvent operator technique for A-maximal $m$-relaxed ${\eta}$-accretive mappings due to Lan et al. and Chang's lemma [13], some new iterative algorithms with mixed errors for finding the approximate solutions of the aforesaid class of nonlinear random equations are constructed. The convergence analysis of the proposed iterative algorithms under some suitable conditions are also studied.

• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.701-705
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• 2011

The paper reviews Cronbaha's coefficient to measure a single source of error. On the contrary to classical measurement theory, the generalizability study can be used in the social survey design to calculate Generalizability Index (GI) and Dependability Index (DI) for measuring multiple sources of errors of behavior evaluation. The study proposes application guidelines to implement R:($A{\times}B$) mixed models that are composed of random factor and fixed factor.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.697-710
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• 2021

Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.923-932
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• 2008

We are likely to face complex multivariate data which can be characterized by having a non-trivial correlation structure. For instance, omitted covariates may simultaneously affect more than one count in clustered data; hence, the modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference. A standard way to insert dependence among counts is to assume that they share some common unobservable variables. For this assumption, we fitted correlated random effect models considering multilevel model. Estimation was carried out by adopting the semiparametric approach through a finite mixture EM algorithm without parametric assumptions upon the random coefficients distribution.

• Journal of Families and Better Life
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• v.33 no.2
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• pp.35-51
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• 2015

The purpose of this study was to provide information for research about residents' opinion toward the physical environments of elderly facilities, through the analysis and investigation on the research methodology of foreign academic journal articles from 1990 to 2014. The study results were as follows: Firstly, purposive sampling was a large majority of both facilities and elderly residents. In quantitative studies, many researchers have conducted simple random, cluster, or stratified sampling. Diverse facilities in area, size, location, and etc. should be considered for participation. The qualifications for residents' participation should be considered as well, so that they all could have autonomy for study participation. Secondly, questionnaire and semi-structured guide were likely to be used in independent and resident care facilities. On the other hand in assisted living and long-term care facilities, open questions and visual material were used as well. A compatible scale should be developed so that elderly having variable functional level could participate independently in the study. Thirdly, in data collection process, compliance with research ethics and well trained interviewer's skill were important for residents' active responses and minimization of response errors. Enough research period of time and mixed study in data collection will decrease the response error.

• Korean Journal of Veterinary Research
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• v.38 no.3
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• pp.544-552
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• 1998

A random study of 574 dairy farms in Gyeongnam area was designed to determine 1) management factors that may be associated with the occurrence of drug residues; 2) the dairy farmer's attitudes and knowledge about residues; 3) how these variables influence the occurrence of residues in dairy cattle. Management factors perceived as having the greatest influence on drug residues in milk were insufficient knowledge about withdrawal periods, errors due to hired help, insufficient identification and record of animals treated for mastitis, dry cow treatment for mastitis, and metritis treatment. Seventy-one percent of farms with residues problem used mixed own feeds compared with twenty-nine percent of farms with premedicated feeds. Factors significantly associated with the occurrence of residues were herd size, increased number of hired persons, increased frequency of use of mixed-own feeds, category of medicated feed, and producer's attitude toward the public health significance of residues. Our findings suggest that residue occurrence was mainly associated with errors due to hired help, insufficient knowledge about withdrawal periods, poor animal identification and records of treatment animals and use of medicated feeds. Any residue avodiance educational program needs to stress how to deal with these factors. This educational program should be directed to dairy farmers and employees, especially temporary employees. In addition, Dairy farmer's attitudes and knowledge about drug residues need to be improved. More evidence on the public healths significance of residues should be available to them. Because belife in importance of public healths concerns was related to successful residue avidance and because 81.3% of the dairy farmers with residue problem thought public healths concerns were less important than economic ones, it would be helpful to provide educational programs specifically directed to this issue. it may be useful to provide programs not only for the dairy farmers but also change of their concerns about on the public healths.