• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2265-2267
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• 2007

The purpose of this work is to compare the local fitting and global fitting approaches while applying regression model to the PTT-BP data for the prediction of exercise blood pressures. We used linear and nonlinear regression models to represent the PTT-BP relationship during exercise. PTT-BP data were acquired both under resting state and also after cycling exercise with several load conditions. PTT was calculated as the time between R-peak of ECG and the peak of differential photo-plethysmogram. For the identification of the regression models, we used local fitting which used only the resting state data and global fitting which used the whole region of data including exercise BP. The results showed that the global fitting was superior to the local fitting in terms of the coefficient of determination and the RMS (root mean square) error between the experimental and estimated BP. The nonlinear regression model which used global fitting showed slightly better performance than the linear one (no significant difference). We confirmed that the wide-range of data is required for the regression model to appropriately predict the exercise BP.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.13-22
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• 2000

We consider local likelihood method with a smoothed version of the model density in stead of an original model density. For simplicity a model is assumed as the log-linear density then we were able to show that the proposed local density estimator is less affected by changes among observations but its bias increases little bit more than that of the currently used local density estimator. Hence if we use the existing method and the proposed method in a proper way we would derive the local density estimator fitting the data in a better way.

• Journal of the Korean Mathematical Society
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• v.47 no.2
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• pp.385-407
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• 2010

To study the relationship between the levels of chemical pollutants and the number of daily total hospital admissions for respiratory diseases and to find the effect of temperature/relative humidity on the admission number, Wong et al. [17] introduced the varying-coefficient single-index model (VCSIM). As pointed out, it is a popular multivariate nonparametric fitting technique. However, the tests of the model have not been very well developed. In this paper, based on the estimators obtained by the local linear technique, the average method and the one-step back-fitting technique in the VCSIM, the generalized likelihood ratio (GLR) tests for varying-coefficient parts on the VCSIM are established. Under the null hypotheses the new proposed GLR tests follow the $\chi^2$-distribution asymptotically with scale constant and degree of freedom independent of the nuisance parameters, known as Wilks phenomenon. Simulations are conducted to evaluate the test procedure empirically. A real example is used to illustrate the performance of the testing approach.

• 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.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.816-824
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• 2009

In the face space, face images due to illumination and pose variations have a nonlinear distribution. Active Appearance Models (AAM) based on the linear model have limits to the nonlinear distribution of face images. In this paper, we assume that a few clusters of face images are given; we build local AAMs according to the clusters of face images, and then select a proper AAM model during the fitting phase. To solve the problem of updating fitting parameters among the models due to the model changing, we propose to build in advance relationships among the clusters in the parameter space from the training images. In addition, we suggest a gradual model changing to reduce improper model selections due to serious fitting failures. In our experiment, we apply the proposed model to Yale Face Database B and compare it with the previous method. The proposed method demonstrated successful fitting results with strongly illuminated face images of deep shadows.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.231-246
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• 2001

Local linear smoothing enjoys several excellent theoretical and numerical properties, an in a range of applications is the method most frequently chosen for fitting curves to noisy data. Nevertheless, it suffers numerical problems in places where the distribution of design points(often called predictors, or explanatory variables) is spares. In the case of univariate design, several remedies have been proposed for overcoming this problem, of which one involves adding additional ″pseudo″ design points in places where the orignal design points were too widely separated. This approach is particularly well suited to treating sparse bivariate design problem, and in fact attractive, elegant geometric analogues of unvariate imputation and interpolation rules are appropriate for that case. In the present paper we introduce and develop pseudo dta rules for bivariate design, and apply them to real data.

• Journal of the Chungcheong Mathematical Society
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• v.22 no.4
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• pp.777-787
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• 2009

In this paper we investigate the upper bound on the Castelnuovo-Mumford regularity of a graded module with linear free presentation. Let M be a finitely generated graded module over a polynomial ring R with zero dimensional support. We prove that if M is generated by elements of degree $d{\geq}0$ with a linear free presentation $$\bigoplus^p{R}(-d-1)\longrightarrow^{\phi}\bigoplus^q{R}(-d){\longrightarrow}M{\longrightarrow}0$$, then the Castelnuovo-Mumford regularity of M is at most d+q-1. As an important application, we can prove vector bundle technique, which was used in [11], [13], [17] as a tool for obtaining several remarkable results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3C
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• pp.234-240
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• 2002

A power amplifier with AlGaAs/InGaAs/GaAs HBT's has been developed for customer premise equipments of the quasi millimeter-wave frequency-band broadband wireless local loop(BWLL) system. Parameters of the linear and nonlinear equivalent circuits for a common base HBT have been extracted by a fitting method. The amplifier has been designed through the linear and nonlinear circuit simulations and fabricated on a ceramic substrate for a hybrid IC. The amplifier has produced a 25.5-dBm output power with 35% power-added efficiency(PAE) at 24.4 GHz and achieved a 7.5-dB linear power gain at 24.8 GHz. In 24.25 ∼24.75 GHz band, the amplifier has exhibited a saturated output over larger than 22 dBm and PAE higher than 25%.

• Journal of Forest and Environmental Science
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• v.34 no.4
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• pp.313-320
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• 2018

Tectona grandis (teak) is one of the most important timber species worldwide and India is one of the major teak growing countries. Though some volume equations were developed for teak in India but the models developed were neither evaluated using robust statistical criteria nor validated. Hence, the objective of this study was to develop statistically tested appropriate volume equation to predict total wood volume (over- and under-bark) for teak trees in Gujarat. A total of 41 trees with age varying from 15 to 33 years and diameter at breast height (dbh) from 7.3 to 30.8 cm were felled for the purpose. Linear and non-linear equations were used to model the relationship of the total wood volume with respect to dbh and total height. The equations tested mostly fitted well to the data. Model evaluation and validation indicated that models should be calibrated with local data for greater accuracy in the prediction.

• Computers and Concrete
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• v.22 no.6
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• pp.527-538
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• 2018

Buckling of longitudinal bars is a brittle failure mechanism, often recorded in reinforced concrete (RC) structures after an earthquake. Studies in the literature highlights that it often occurs when steel is in the post elastic range, by inducing a modification of the engineered stress-strain law of steel in compression. A proper evaluation of this effect is of fundamental importance for correctly evaluating capacity and ductility of structures. Significant errors can be obtained in terms of ultimate bending moment and curvature ductility of an RC section if these effects are not accounted, as well as incorrect evaluations are achieved by non-linear static analyses. This paper presents a numerical investigation aiming to evaluate the engineered stress-strain law of reinforcing steel in compression, including second order effects. Non-linear FE analyses are performed under the assumption of local buckling. A role of key parameters is evaluated, making difference between steel with strain hardening or with perfectly plastic behaviour. Comparisons with experimental data available in the literature confirm the accuracy of the achieved results and make it possible to formulate recommendations for design purposes. Finally, comparisons are made with analytical formulations available in the literature and based on obtained results, a modification of the stress-strain law model of Dhakal and Maekawa (2002) is proposed for fitting the numerical predictions.