• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.295-304
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• 2019

This article deals with threshold-asymmetric volatility models for over-dispersed and zero-inflated time series of count data. We introduce various threshold integer-valued autoregressive conditional heteroscedasticity (ARCH) models as incorporating over-dispersion and zero-inflation via conditional Poisson and negative binomial distributions. EM-algorithm is used to estimate parameters. The cholera data from Kolkata in India from 2006 to 2011 is analyzed as a real application. In order to construct the threshold-variable, both local constant mean which is time-varying and grand mean are adopted. It is noted via a data application that threshold model as an asymmetric version is useful in modelling count time series volatility.

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.1
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• pp.83-89
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• 1988

Optimal screening procedures with dichotomous performance variable T and continuous screening variable X are presented for assuring with a specified degree of confidence that at least ${\ell}$ out of m items found acceptable in screening inspection are conforming. It is assumed that T is a Bernoulli random variable and that the conditional distribution of X given T=t is normal. When m is also to be determined, optimal m and cut-off value of X minimizing the total expected cost are obtained. Cases of known and unknown parameters are considered and for unknown parameter cases, Bayesian approaches are used to find the optimal screening procedures.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.131-140
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• 2018

Precipitation is a key variable to enhance the understanding of water cycle system and secure and manage the water resources efficiently. In this study, we evaluated the feasibility of GPM precipitation datasets through comparison with the 92 ASOS sites in South Korea during 2015. Additionally, three merging techniques (i.e., Geographical Differential Analysis, Geographical Ratio Analysis, Conditional Merging) were applied to improve accuracy of precipitation by fusing the advantages from point and satellite-based datasets. The results of this study are as follows. 1) GPM dataset indicated slightly overestimation with compared ASOS dataset, especially high uncertainties in summer season. 2) Validation of three merging techniques through jackniffe cross-validation showed that uncertainty were decreased as the spatial resolution increased. Especially, conditional merging showed the best performance among three methods.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.103-110
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• 2001

This paper considers a multistage experiment. Response scales can be same or different from stage to stage. When variables are of nested structure, the response variable at each stage can be defined conditionally. For analysing such data with compound scales, this paper suggests a sequnce of dependence models and shows how to set up a sequence of models for the driver's liscense test data.

• Journal of the Korean Mathematical Society
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• v.55 no.2
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• pp.447-469
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• 2018

Types (over parameters) in the theory of atomless random variable structures correspond precisely to (conditional) distributions in probability theory. Moreover, the logic (resp. metric) topology on the type space corresponds to the topology of weak (resp. strong) convergence of distributions. In this paper, we study metrics between types. We show that type spaces under $d^{\ast}-metric$ are isometric to Wasserstein spaces. Using optimal transport theory, two formulas for the metrics between types are given. Then, we give a new proof of an integral formula for the Wasserstein distance, and generalize some results in optimal transport theory.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.973-981
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• 2010

Quantile regression investigates the quantiles of the conditional distribution of a response variable given a set of covariates. M-quantile regression extends this idea by a "quantile-like" generalization of regression based on influence functions. In this paper we propose a new method of estimating M-quantile regression functions, which uses kernel machine technique. Simulation studies are presented that show the finite sample properties of the proposed M-quantile regression.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.265-268
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• 2002

For accurate interpretation, ECG signal is measured by using 12 leads method. We look shape of Measured ECG signal and decide whether interpretation is accurate or not. In this paper, we propose new effective fuzzy decision system which uses fuzzy rules and membership functions for more accurate of ECG wave. We used PR interval, QRS interval and QRS axis as conditional variables for designing fuzzy rules. And decision rule of conclusion variable is determined by (sinus rhythm), (sinus rhythm+left deviation), (sinus rhythm+right deviation) and (sinus rhythm+negative axis). Experimental results showed our system made numerically easy decision possible and had advantage of simple design method.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.77-91
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• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

• Journal of the Korean Society of Combustion
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• v.6 no.2
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• pp.43-49
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• 2001

Instantaneous, three-dimensional scalar dissipation rates of the reaction progress variable are measured in turbulent premixed Bunsen flames of lean hydrocarbon/air mixtures with the two-sheet, two-dimensional Rayleigh scattering technique. The flames investigated are located in the turbulent flame-front regime on a newly proposed combustion diagram for premixed flames. The conditionally-averaged mean scalar dissipation rates, $N_{\zeta}$ are found to be lower than the calculated laminar values, indicating a locally broadened flame front. In agreement with previous measurements, the maximum of $N_{\zeta}$, decreases strongly with increasing Karlovitz numbers. The conditional probability density functions are close to a log-normal distribution for scalar dissipation rates conditioned at the progress variable value where the scalar dissipation is maximum in unstretched laminar flame calculations. The time scale for the Favre-averaged mean scalar dissipation rate decreases in general across the turbulent flame brush from the unburnt to burnt side.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.97-102
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• 1997

A new method based on the alternating conditional expectation (ACE) algorithm is developed to calculate axial shape index (ASI) for the 3-level excore detector. The ACE algorithm, a type of non-parametric regression algorithms, yields an optimal relationship between a dependent variable and multiple independent variables. In this study, the simple correlation between ASI and excore detector signals is developed using the Younggwang nuclear power plant unit 3 (YGN-3) data without any preprocessing on the relationships between independent variables and dependent variable. The numerical results show that simple correlations exist between the three excore signals and ASI of the core. The accuracy of the new method is much better than those of the current CPC and COLSS algorithms.