• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.303-312
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• 1995

We consider the following type of general semi-parametric non-linear regression model : $y_i = f_i(\theta) + \epsilon_i, i=1, \cdots, n$ where ${f_i(\cdot)}$ represents the set of non-linear functions of the unknown parameter vector $\theta' = (\theta_1, \cdots, \theta_p)$ and ${\epsilon_i}$ represents the set of measurement errors with unknown distribution. Under suitable finite-sample, small-dispersion asymptotic framework, we derive a general lower bound for the asymptotic mean squared error (AMSE) matrix of the Gauss-consistent estimator of $\theta$. We then prove the fundamental result that the general non-linear least squares estimator (NLSE) is an optimal estimator within the class of all regular Gauss-consistent estimators irrespective of the type of the distribution of the measurement errors.

• 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 $\varepsilon$.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.4
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• pp.49-59
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• 2016

Naval combat system developed in-country is progressing at an alarming rate since 2000. ROK navy will be achieved all vessels that have combat system in the near future. The importance of System Engineering and Integrated Logistics Support based on reliability analysis is increasing. However, reliability analysis that everyone trusted and recognized is not enough and applied practically for development of Defense Acquisition Program. In particular, Existing Reliability Analysis is focusing on reliability index (Mean Time Between Failure (MTBF) etc.) for policy decision of defense improvement project. Most of the weapon system acquisition process applying in the exponential distribution simply persist unreality due to memoryless property. Critical failures are more important than simple faults to ship's operator. There are no confirmed cases of reliability analysis involved with critical failure that naval ship scheduler and operator concerned sensitively. Therefore, this study is focusing on Mean Time To Critical Failure (MTTCF), reliability on specific time and Operational Readiness Float (ORF) requirements related to critical failure of Patrol Killer Guided missile (PKG) combat system that is beginning of naval combat system developed in-country. Methods of analysis is applied parametric and non-parametric statistical techniques. It is compared to the estimates and proposed applications. The result of study shows that parametric and non-parametric estimators should be applied differently depending on purpose of utilization based on test of normality. For the first time, this study is offering Reliability of ROK Naval combat system to stakeholders involved with defense improvement project. Decision makers of defense improvement project have to active support and effort in this area for improvement of System Engineering.

• Industry Promotion Research
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• v.7 no.4
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• pp.1-8
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• 2022

There are many one-shot devices that are used once and thrown away. One-shot devices such as firecrackers and ammunition are typical, and they are stored for a while after manufacture and then disposed of after use when necessary. However, unlike general operating systems, these one-shot devices have not been properly evaluated. This study first examines what the government does to secure reliability in the case of ammunition through ammunition stockpile reliability program. Next, in terms of statistical analysis, we show what the reliability analysis methods are for one-shot devices such as ammunition. Specifically, we show that it is possible to know the level of reliability if sampling inspection plan such as KS Q 0001 which is acceptance sampling plan by attributes is used. Next, non-parametric and parametric methods are introduced as ways to determine the storage reliability of ammunition. Among non-parametric methods, Kaplan-Meier method can be used since it can also handle censored data. Among parametric methods, Weibull distribution can be used to determine the storage reliability of ammunition.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.327-336
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• 2001

This work concerns estimating a regression function, which is not linear, using aggregate data. In much of the empirical research, data are aggregated for various reasons before statistical analysis. In a traditional parametric approach, a linear estimation of the non-linear function with aggregate data can result in unstable estimators of the parameters. More serious consequence is the bias in the estimation of the non-linear function. The approach we employ is the kernel regression smoothing. We describe the conditions when the aggregate data can be used to estimate the regression function efficiently. Numerical examples will illustrate our findings.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.11
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• pp.1525-1528
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• 2011

The purpose of this study was to detect SNPs that were responsible for a carcass trait in Hanwoo populations. A non-parametric model applying a restricted partition method (RPM) was used, which exploited a partitioning algorithm considering statistical criteria for multiple comparison testing. Phenotypic and genotypic data were obtained from the Hanwoo Improvement Center, National Agricultural Cooperation Federation, Korea, in which the pedigree structure comprised 229 steers from 16 paternal half-sib proven sires that were born in Namwon or Daegwanryong livestock testing station between spring of 2002 and fall of 2003. A carcass trait, longissimus dorsi muscle area for each steer was measured after slaughter at approximately 722 days. Three SNPs (19_1, 18_4 and 28_2) near the microsatellite marker ILSTS035 on BTA6, around which the quantitative trait loci (QTL) for meat quality were previously detected, were used in this study. The RPM analyses resulted in two significant interaction effects between SNPs (19_1 and 18_4) and (19_1 and 28_2) at ${\alpha}$ = 0.05 level. However, under a general linear (parametric) model no interaction effect between any pair of the three SNPs was detected, while only one main effect for SNP19_1 was found for the trait. Also, under another non-parametric model using a multifactor dimensionality reduction (MDR) method, only one interaction effect of the two SNPs (19_1 and 28_2) explained the trait significantly better than the parametric model with the main effect of SNP19_1. Our results suggest that RPM is a good alternative to model choices that can find associations of the interaction effects of multiple SNPs for quantitative traits in livestock species.

• International Journal of Reliability and Applications
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• v.10 no.2
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• pp.81-88
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• 2009

A non-parametric procedure is presented for testing exponentially against used better than aged in convex ordering class (UBAC) of life distributions based on u-test. Convergence of the proposed statistic to the normal distribution is proved. Selected critical values are tabulated for sample sizes 5(5)40. The Pitman asymptotic relative efficiency of my proposed test to tests of other classes is studied. An example of 40 patients suffering from blood cancer disease demonstrates practical application of the proposed test.

• Journal of the Korean Statistical Society
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• v.33 no.3
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• pp.303-312
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• 2004

The concept of dispersion is intrinsic to the theory and practice of statistics. A formulation of the concept of dispersion can be obtained by comparing the probability of intervals centered about a location parameter. This is the peakedness ordering introduced first by Birnbaum (1948). We consider statistical inference concerning peakedness ordering between two arbitrary distributions. We propose non parametric maximum likelihood estimators of two distributions under peakedness ordering and a likelihood ratio test for equality of dispersion in the sense of peakedness ordering.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1289-1312
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• 2015

This paper studies the comovement of industry defaults among listed companies. Rank correlation coefficients of Spearman's ${\rho}$ and Kendall's ${\tau}$ measure the concordance of default. These non-parametric coefficients do not require distributional assumptions and are easily used even with less data and extreme values. This study predicts a future financial crisis by looking at the comovement of industry defaults. We expect our analyses will aid market participants (including company executives) in making investment or risk management decisions.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.2999-3029
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• 2024

To improve the predictive capability of a nuclear thermal hydraulic safety analysis code by developing a better constitutive equation for individual phenomenon has been the general research direction until now. This paper proposes a new method to directly use complex experimental data obtained from integral effect test (IET) to improve constitutive models holistically and simultaneously. The method relies on the sensitivity of a simulation result of IET data to the multiple constitutive equations utilized during the simulation, and the sensitivity of individual model determines the direction of modification for the constitutive model. To develop a robust and generalized method, a clustering algorithm using an artificial neural network, sample space size determination using non-parametric statistics, and sampling method of Latin hypercube sampling are used in a combined manner. The value of the proposed methodology is demonstrated by applying the method to the ATLAS DSP-05 IET experiment. A sensitivity of each observation parameter to the constitutive models is analyzed. The new methodology suggested in the study can be used to improve the code prediction results of complex IET data by identifying the direction for constitutive equations to be modified.