• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.141-146
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• 2002

In this article we consider the problem of constructing confidence intervals for a linear regression model with nested error structure. A popular approach is the likelihood-based method employed by PROC MIXED of SAS. In this paper, we examine the ability of MIXED to produce confidence intervals that maintain the stated confidence coefficient. Our results suggest the intervals for the regression coefficients work well, but the intervals for the variance component associated with the primary level cannot be recommended. Accordingly, we propose alternative methods for constructing confidence intervals on the primary level variance component. Computer simulation is used to compare the proposed methods. A numerical example and SAS code are provided to demonstrate the methods.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.2
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• pp.160-165
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• 2007

Dispersion parameters for the number of piglets born alive were estimated using a repeatability and random regression model. Six sow breeds/lines were included in the analysis: Swedish Landrace, Large White and both crossbred lines between them, German Landrace and their cross with Large White. Fixed part of the model included sow genotype, mating season as month-year interaction, parity and weaning to conception interval as class effects. The age at farrowing was modelled as a quadratic regression nested within parity. The previous lactation length was fitted as a linear regression. Random regressions for parity on Legendre polynomials were included for direct additive genetic, permanent environmental, and common litter environmental effects. Orthogonal Legendre polynomials from the linear to the cubic power were fitted. In the repeatability model estimate of heritability was 0.07, permanent environmental effect as ratio was 0.04, and common litter environmental effect as ratio was 0.01. Estimates of genetic parameters with the random regression model were generally higher than in the repeatability model, except for the common litter environmental effect. Estimates of heritability ranged from 0.06 to 0.10. Permanent environmental effect as a ratio increased along a trajectory from 0.03 to 0.11. Magnitudes of common litter effect were small (around 0.01). The eigenvalues of covariance functions showed that between 7 and 8 % of genetic variability was explained by individual genetic curves of sows. This proportion was mainly covered by linear and quadratic coefficients. Results suggest that the random regression model could be used for genetic analysis of litter size.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.449-461
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• 1999

본 논문에서는 패널회귀모형에서 내부변환(within transformation) 추정량을 이용하여 회귀계수에 대한 정확한 신뢰구간을 제시하였다. 아울러 이러한 신뢰구간의 효율성을 신뢰계수(confidence coefficient)와 신뢰구간의 평균길이(average length of confidence interval)을 사용하여 모의실험을 통하여 다른 근사적 신뢰구간들과 비교하였다. 실험결과, 내부변환추정량을 이용한 신뢰구간은 다른 근사적 신뢰구간들에 비해 명목신뢰계수를 정확히 유지하였고, 신뢰구간의 평균길이도 다른 방법들에 비해 짧은 결과를 보았다.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.445-455
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• 1999

We modify the Gompertz regression model for estimation of cure rates from pediatric clinical trials by assuming different hazard rates on the different periods. A treatment period may be divided by the stages of treatments under the different treatment arms. The piecewise Gompertz models provide an efficient method for estimation of the cure rates and a method for testing the difference of the treatment effects in the given interval.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.1
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• pp.111-120
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• 2018

The purpose of this study was to develop a ridge regression (RR) model to estimate BOD and TP load using runoff weighted value. The concept of runoff weighted value, based on distributed curve-number (CN), was introduced to reflect the impact of land covers on runoff. The estimated runoff depths by distributed CN were closer to the observed values than those by area weighted mean CN. The RR is a technique used when the data suffers from multicollinearity. The RR model was developed for five flow duration intervals with the independent variables of daily runoff discharge of seven land covers and dependent variables of daily pollutant load. The RR model was applied to Heuk river watershed, a subwatershed of the Han river watershed. The variance inflation factors of the RR model decreased to the value less than 10. The RR model showed a good performance with Nash-Sutcliffe efficiency (NSE) of 0.73 and 0.87, and Pearson correlation coefficient of 0.88 and 0.93 for BOD and TP, respectively. The results suggest that the methods used in the study can be applied to estimate pollutant load of different land cover watersheds using limited data.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.11-20
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• 2017

This study is to estimate the spatial soil moisture using multiple linear regression model (MLRM) and 15 minutes interval Land Surface Temperature (LST) data of Communication, Ocean and Meteorological Satellite (COMS). For the modeling, the input data of COMS LST, Terra MODIS Normalized Difference Vegetation Index (NDVI), daily rainfall and sunshine hour were considered and prepared. Using the observed soil moisture data at 9 stations of Automated Agriculture Observing System (AAOS) from January 2013 to May 2015, the MLRMs were developed by twelve scenarios of input components combination. The model results showed that the correlation between observed and modelled soil moisture increased when using antecedent rainfalls before the soil moisture simulation day. In addition, the correlation increased more when the model coefficients were evaluated by seasonal base. This was from the reverse correlation between MODIS NDVI and soil moisture in spring and autumn season.

• Journal of Environmental Science International
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• v.21 no.5
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• pp.613-622
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• 2012

A pilot-scale pulse-jet bagfilter was designed, built and tested for the effects of four operating conditions (filtration velocity, inlet dust concentration, pulse pressure, and pulse interval time) on the total system pressure drop, using coke dust from a steel mill factory. Two models were used to predict the total pressure drop according to the operating conditions. These model parameters were estimated from the 180 experimental data points. The empirical model (EM) with filtration velocity, areal density, inlet dust concentration, pulse interval time and pulse pressure shows the best correlation coefficient (R=0.971) between experimental data and model predictions. The empirical model was used as it showed higher correlation coefficient (R=0.971) compared to that of the Multivariate linear regression(MLR) (R=0.961). The minimum pulse pressure predicted by empirical model (EM) was 5kg/$cm^2$.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.21-35
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• 1996

In this paper, estimation and prediction procedures are discussed for grneral situation in which the failure time follows the independent density $f_{i}({\varepsilon}_{i})$ for the accelerated life testing under Type II censoring. In the context of accelerated life test experiment, procedures are given for estimating the parameters in the Eyring model, and for estimating mean life at a given future stress level. The procedures given are conditional confidence interval procedures, obtained by conditioning on ancillary statistics. A comparison is made of these procedures and procedures based on asymptotic properties of the maximum, likelihood estimates.

• Journal of the Korean Statistical Society
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• v.25 no.4
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• pp.567-576
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• 1996

The multiprocess dynamic survival model is proposed for the application of the regression model on the analysis of survival data with time-varying effects of covariates : where the survival data consists of numbers of deaths at certain time-points. The algorithm for the recursive estimation of a time-varying parameter vector is suggested. Also the algorithm of forecasting of numbers of deaths of each group in the next time interval based on the information gathered until the end of current time interval is suggested.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.127-135
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• 2001

In this paper, we consider a simple method for testing the assumption of independent censoring on the basis of a Cox proportional hazards regression model with a time-dependent covariate. This method involves a two-stage sampling in which a random subset of censored observations is selected and followed-up until their true survival times are observed. Lee and Wolfe(1998) proposed an adjusted estimate of the survivor function for the dependent censoring under a proportional hazards alternative. This paper extends their result to obtain a bootstrap confidence interval for the adjusted survivor function under the dependent censoring. The proposed procedure is illustrated with an example of a clinical trial for lung cancer analysed in Lee and Wolfe(1998).