• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.531-536
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• 2022

This paper compared the performance of the parametric method and the nonparametric method when estimating the distribution for the tail of the distribution with heavy tails. For the parametric method, the generalized extreme value distribution and the generalized Pareto distribution were used, and for the nonparametric method, the kernel density estimation method was applied. For comparison of the two approaches, the results of function estimation by applying the block maximum value model and the threshold excess model using daily fine dust public data for each observatory in Seoul from 2014 to 2018 are shown together. In addition, the area where high concentrations of fine dust will occur was predicted through the return level.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.421-429
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• 2008

We consider a general semiparametric additive risk model that consists of three components. They are parametric, purely and smoothly nonparametric components. In parametric component, time dependent term is known up to proportional constant. In purely nonparametric component, time dependent term is an unknown function, and time dependent term in smoothly nonparametric component is an unknown but smoothly function. As an estimation method of this model, we use the weighted least square estimation by Huffer and McKeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

• Journal of the Korean Statistical Society
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• v.28 no.3
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• pp.359-370
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• 1999

We considered a change-point hazard rate model generalizing constant hazard rate model. This type of model is very popular in the sense that the Weibull and exponential distributions formulating survival time data are the special cases of it. Maximum likelihood estimation and the asymptotic properties such as the consistency and its limiting distribution of the change-point estimator were discussed. A parametric bootstrap method for finding confidence intervals of the unknown change-point was also suggested and the proposed method is explained through a practical example.

• Journal of the Korea Safety Management & Science
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• v.9 no.2
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• pp.161-169
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• 2007

This paper presents three methods for expression of reliability measures for large and small data. First method is to express parametric estimation of cardinal reliability measure data for large sample, which requires numerous sample. Second is to obtain nonparametric distribution classification of ordinal reliability measure data for small sample. However it is difficult for field user to understand this method. Last method is to acquire parametric estimation of ordinal reliability measure data for small data. Because this method requires small sample and is comprehensive, we recommend this one among the proposed methods. Various reliability rank measures are presented.

• Journal of Korea Technology Innovation Society
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• v.6 no.3
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• pp.279-297
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• 2003

The purpose of this paper is to analyze the determinants of company R＆D investment with zero observations by using the data of R＆D Scoreboard published by Ministry of Science and Technology(2002). Conventional parametric approach to dealing with zero investments is not robust to heteroscedastic and/or non-normal error structure. Thus, this study applies symmetrically trimmed least squares(STLS) estimation as a semi-parametric approach to dealing with zero R&D investments. The result of specification test indicates the semi-parametric approach outperforms the parametric approach significantly. Moreover, the results of the study provide various implications as summarized below. The R&D investment of IT company is larger than that of non-IT company. The R&D investment has a positive relation to foreigners' investment ratio. The higher degree of financial self-reliance is, the larger the R&D investment is. Firm size variables such as sales amount and the number of workers are positively related to R&D investment. The sales elasticity of R&D investment is larger than one. However, the workers elasticity of R&D investment is smaller than one.

• The Korean Journal of Physiology and Pharmacology
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• v.13 no.5
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• pp.367-371
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• 2009

The estimation of 90% parametric confidence intervals (CIs) of mean AUC and Cmax ratios in bioequivalence (BE) tests are based upon the assumption that formulation effects in log-transformed data are normally distributed. To compare the parametric CIs with those obtained from nonparametric methods we performed repeated estimation of bootstrap-resampled datasets. The AUC and Cmax values from 3 archived datasets were used. BE tests on 1,000 resampled data sets from each archived dataset were performed using SAS (Enterprise Guide Ver.3). Bootstrap nonparametric 90% CIs of formulation effects were then compared with the parametric 90% CIs of the original datasets. The 90% CIs of formulation effects estimated from the 3 archived datasets were slightly different from nonparametric 90% CIs obtained from BE tests on resampled datasets. Histograms and density curves of formulation effects obtained from resampled datasets were similar to those of normal distribution. However, in 2 of 3 resampled log (AUC) datasets, the estimates of formulation effects did not follow the Gaussian distribution. Bias-corrected and accelerated (BCa) CIs, one of the nonparametric CIs of formulation effects, shifted outside the parametric 90% CIs of the archived datasets in these 2 non-normally distributed resampled log (AUC) datasets. Currently, the 80~125% rule based upon the parametric 90% CIs is widely accepted under the assumption of normally distributed formulation effects in log-transformed data. However, nonparametric CIs may be a better choice when data do not follow this assumption.

• Environmental and Resource Economics Review
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• v.25 no.1
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• pp.1-25
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• 2016

This study investigated the variability of WTP estimates(i.e. mean or median) with ad hoc assumptions of specific parametric probability distributions(i.e. normal, logistic, lognormal, and exponential distribution) to estimate WTP function using dichotomous choice CV data on mortality risk reduction. From the perspective of policy decision, the variability of these WTP estimates are intolerable in comparison with those of Turnbull nonparametric estimation method which is free from ad hoc distribution assumptions. The Turnbull nonparametric estimation can avoid a kind of misspecification bias due to ad hoc assumption of specific parametric distributions. Furthermore, the WTP estimates by Turnbull nonparametric estimation are robust because the similar estimates are elicited from a dichotomous choice or double dichotomous choice CV data, and the statistically significant WTP estimates can be obtained even though it is not possible by parametric estimation methods. If there are considerable variability among those WTP estimates by parametric estimation methods in condition with no criteria of model adequacy, the mean WTPs from Turnbull nonparametric estimation can be the robust estimates without ad hoc assumptions, which can avoid controversial issues in the perspective of policy decisions.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.825-833
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• 2013

An estimation of confidence intervals is essential to calculate uncertainty for greenhouse gases inventory. It is generally assumed that the population has a normal distribution for the confidence interval of parameters. However, in case data distribution is asymmetric, like nonnormal distribution or positively skewness distribution, the traditional estimation method of confidence intervals is not adequate. This study compares two estimation methods of confidence interval; parametric and non-parametric method for exponential distribution as an asymmetric distribution. In simulation study, coverage probability, confidence interval length, and relative bias for the evaluation of the computed confidence intervals. As a result, the chi-square method and the standardized t-bootstrap method are better methods in parametric methods and non-parametric methods respectively.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.2
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• pp.41-51
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• 2014

This paper describes a method to estimate the noise power using the minimum statistics approach, which was originally proposed for audio processing. The proposed minimum statistics-based method separates a noisy image into multiple frequency bands using the three-level discrete wavelet transform. By assuming that the output of the high-pass filter contains both signal detail and noise, the proposed algorithm extracts the region of pure noise from the high frequency band using an appropriate threshold. The region of pure noise, which is free from the signal detail part and the DC component, is well suited for minimum statistics condition, where the noise power can be extracted easily. The proposed algorithm reduces the computational load significantly through the use of a simple processing architecture without iteration with an estimation accuracy greater than 90% for strong noise at 0 to 40dB SNR of the input image. Furthermore, the well restored image can be obtained using the estimated noise power information in parametric image restoration algorithms, such as the classical parametric Wiener or ForWaRD image restoration filters. The experimental results show that the proposed algorithm can estimate the noise power accurately, and is particularly suitable for fast, low-cost image restoration or enhancement applications.

• Journal of KIBIM
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• v.8 no.1
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• pp.56-62
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• 2018

There exists no precedented case of quantity take-off, using parametric modeling, from BIM-based irregular structures. Civil 3D provides earthwork quantity take-off based on surface modeling. Generally, designers should enter data into the specification additionally after extracting quantity estimation from earthwork modeling design. The objective of this report is to suggest the method from quantity take-off to specification of BIM-based earthwork quantities. We intend to investigate earthwork take-off method by Civil3D and explain why parametric information extraction is required for quantity estimation and specification and how information of earthwork quantity based on solid and surface modeling is connected to open quantity take-off module. It is highly expected that this suggestion would be the practical methodology of earthwork quantity take-off and specification in the field of civil engineering.