• KOREAN JOURNAL OF CROP SCIENCE
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• v.36 no.2
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• pp.97-106
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• 1991

Two dimensional non-parametric model using daily mean temperature and daylength as predictor variables was established and daily developmental rates (DVR) for the period of seedling emergence to heading were estimated for 26 rice cultivars by using data from field direct seeding dates and short-day treatments experiment carried out at experimental farm of Seoul National University in 1990. Three existing parametric models were tested for the comparision of predictability with non-parametric model. The non-parametric model was found to be superior to parametric models in predicting heading date. The developmetal indice(DVI) at heading date, cummulative DVR's from seedling emergence showed 0.5 to 2.2 percent of coefficient of variations. The non-parametric model revealed errors of 0 to three days in 11 varieties when applied to data independent of those used in estimating DVR.

• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.13-20
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• 1994

The spectrum estimation methods of random processes are expressed in this paper. Beginning with the basic theory, non-parametric and parametric methods are overviewed. As to non-parametric method, numerical calculation method is also discussed. As to parametric method, AR model is a very famous and effective model representing random process. Estimation methods of AR parameters which have been proposed are mentioned here. Wavelet analysis is a recently interested technique in signal processing. An application of wavelet analysis is also shown.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.701-717
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• 2017

The interest of Korean society and government on future demographic structures is increasing due to rapid aging. Korea's mortality rate is decreasing, but the declined gap is variable. In this study, we compare the Lee-Carter, Lee-Miller, Booth-Maindonald-Smith model and functional data model (FDM) as well as Coherent FDM using non-parametric smoothing technique. We are then examine a reasonable model for projecting on mortality declined rate trend in terms of accuracy of mortality rate by ages and life expectancy. The possibility of using non-parametric techniques for the prediction of mortality in Korea was also examined. Based on the analysis results, FDM and Coherent FDM, which uses the non-parametric technique and reflects the trend of recent data, are excellent. As a result, FDM and Coherent FDM are good fit, and predictability is also excellent assuming no significant future changes.

• CDE review
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• v.3 no.3
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• pp.36-45
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• 1997

This paper presents the research on the development of Generic modeling system as a 3D CAD customizing system for the elevator design. This system enables to create a generic model which gives birth to many models. The generic model is expressed as a non-scale model or a standard model in this paper. Using the parametric design techniques, all their relationships are explicitly represented in the tables, not represented in implicit embedded coding. Owing to this method, designers can easily extend the generic model to contain more model families. All parametric relations are stored in relational database. A designer can retrieve various models from a generic model automatically, by using some key input values.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.171-174
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• 1998

A class of non-parametric detectors based on quantized m-dimensional noise sample space is introduced. Due to assuming the nongaussian noise as a channel model, it is not easy to design the detector through estimating the unknown functional form of noise; instead equiprobably partitioning m-dimensional noise into a finite number of regions, using a VQ and quantiles obtained by RMSA algorithm is used in this paper to design detectors. To show the comparison of performance between single sample detector and system suggested here, Monte-Carlo simulations were used. The effect of signal pulse shape on the receiver performance is analyzed too.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.1-49
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• 2001

The skin color model is a very important concept in face detection, face recognition and face tracking. Usually, this model is obtained by estimating a probability density function of skin color distribution. In many cases, it is assumed that the underlying density function follows a Gaussian distribution. In this paper, a new method for non-parametric estimation of the probability density function, by using feed-forward neural network, is used to estimate the underlying skin color model. By using this method, the resulting skin color model is better than the Gaussian estimation and substantially approaches the real distribution. Applications to face detection and face ...

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.677-692
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• 2018

The total fertility rate of Korea was 1.05 in 2017, showing a return to the 1.08 level in the year 2005. 1.05 is a very low fertility level that is far from replacement level fertility or safety zone 1.5. The number may indicate a low fertility trap. It is therefore important to predict fertility than at any other time. In the meantime, we have predicted the age-specific fertility rate and total fertility rate by various statistical methods. When the data trend is disconnected or fluctuating, it applied a nonparametric method applying the smoothness and weight. In addition, the Bayesian method of using the pre-distribution of fertility rates in advanced countries with reference to the three-stage transition phenomenon have been applied. This paper examines which method is reasonable in terms of precision and feasibility by applying estimation, forecasting, and comparing the results of the recent variability of the Korean fertility rate with parametric, non-parametric and Bayesian methods. The results of the analysis showed that the total fertility rate was in the order of KOSTAT's total fertility rate, Bayesian, parametric and non-parametric method outcomes. Given the level of TFR 1.05 in 2017, the predicted total fertility rate derived from the parametric and nonparametric models is most reasonable. In addition, if a fertility rate data is highly complete and a quality is good, the parametric model approach is superior to other methods in terms of parameter estimation, calculation efficiency and goodness-of-fit.

• Journal of the Korean Mathematical Society
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• v.44 no.3
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• pp.525-539
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• 2007

Consider the regression model $Y_i=g(x_i)+e_i\;for\;i=1,\;2,\;{\ldots},\;n$, where: (1) $x_i$ are fixed design points, (2) $e_i$ are independent random errors with mean zero, (3) g($\cdot$) is unknown regression function defined on [0, 1]. Under $Y_i$ are censored randomly, we discuss the asymptotic normality of the weighted kernel estimators of g when the censored distribution function is known or unknown.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.1358-1365
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• 2010

Estimating condition of slope is difficult because of nonlinear time dependency and seasonal effects, which affect the displacements. Displacements and displacement patterns of landslides are highly variable in time and space, and a unique approach cannot be defined to model landslide movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. In the non-parametric approaches, no physical assumptions of target systems are required. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, non-parametric approaches are advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured. Non-parametric approaches are consequently more flexible in modeling than parametric approaches. This method is expected to be a useful tool for the slope management of and alarm systems.