• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.237-245
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• 2007

Nonlinear asymmetric time series models have the growing interest in econometrics and finance. Threshold model is one of the successful asymmetric model. We consider a smooth transition ARMA model which converges a.s. to a threshold ARMA model and show that the smooth transition ARMA model admits a stationary measure, provided a suitable condition on the coefficients of the autoregressive parts of the different regimes is satisfied. Stationarity of a smooth transition GARCH model is also obtained.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.177-187
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• 2020

This article is concerned with the issue of forecasting and evaluation of threshold-asymmetric volatility models for time series of count data. In particular, threshold integer-valued models with conditional Poisson and conditional negative binomial distributions are highlighted. Based on the parametric bootstrap method, some evaluation measures are discussed in terms of one-step ahead forecasting. A parametric bootstrap procedure is explained from which directional measure, magnitude measure and expected cost of misclassification are discussed to evaluate competing models. The cholera data in Bangladesh from 1988 to 2016 is analyzed as a real application.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.713-722
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• 2020

Contrasted with the standard symmetric GARCH models, we consider a broad class of threshold-asymmetric models to analyse financial time series exhibiting asymmetric volatility. By further introducing power transformations, we add more flexibilities to the asymmetric class, thereby leading to power transformed and asymmetric volatility models. In particular, the paper is concerned with the nonstationary volatilities in which conditions for integrated volatility and explosive volatility are separately discussed. Dow Jones Industrial Average is analysed for illustration.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.733-737
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• 2011

This paper has studied subthreshold characteristics for double gate(DG) MOSFET using Gaussian function in solving Poisson's equation. Typical two dimensional analytical transport models have been presented for symmetrical Double Gate MOSFETs (DGMOSFETs). Subthreshold swing and threshold voltage are very important factors for digital devices because of determination of ON and OFF. In general, subthreshold swings have to be under 100mV/dec, and threshold voltage roll-off small in short channel devices. These models are used to obtain the change of subthreshold swings and threshold voltage for DGMOSFET according to channel doping profiles. Also subthreshold swings and threshold voltages have been analyzed for device parameters such as channel length, channel thickness and channel doping profiles.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2079-2088
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• 2014

In this paper, we propose new physically based threshold voltage models for short channel Surrounding Gate Silicon Nanowire Transistor with two different geometries. The model explores the impact of various device parameters like silicon film thickness, film height, film width, gate oxide thickness, and drain bias on the threshold voltage behavior of a cylindrical surrounding gate and rectangular surrounding gate nanowire MOSFET. Threshold voltage roll-off and DIBL characteristics of these devices are also studied. Proposed models are clearly validated by comparing the simulations with the TCAD simulation for a wide range of device geometries.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.8
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• pp.1085-1090
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• 2002

Genetic parameters for birth weights (BWT), calving ease scores observed from calves born by heifers (CEH), and calving ease scores observed from calves born by cows (CEC) were estimated using Bayesian methodology with Gibbs sampling in different threshold animal models. Data consisted of 77,458 records for calving ease scores and birth weights in Gelbvieh cattle. Gibbs samplers were used to obtain the parameters of interest for the categorical traits in two univariate threshold animal models, a bivariate threshold animal model, and a three-trait linear-threshold animal model. Samples of heritabilities and genetic correlations were calculated from the posterior means of dispersion parameters. In a univariate threshold animal model with CEH (model 1), the posterior means of heritabilities for calving ease was 0.35 for direct genetic effects and 0.18 for maternal genetic effects. In the other univariate threshold model with CEC (model 2), the posterior means of heritabilities of CEC was 0.28 for direct genetic effects and 0.18 for maternal genetic effects. In a bivariate threshold model with CEH and CEC (model 3), heritability estimates were similar to those in unvariate threshold models. In this model, genetic correlation between heifer calving ease and cow calving ease was 0.89 and 0.87 for direct genetic effect and maternal genetic effects, respectively. In a three-trait animal model, which contained two categorical traits (CEH and CEC) and one continuous trait (BWT) (model 4), heritability estimates of CEH and CEC for direct (maternal) genetic effects were 0.40 (0.23) and 0.23 (0.13), respectively. In this model, genetic correlation estimates between CEH and CEC were 0.89 and 0.66 for direct genetic effects and maternal effects, respectively. These estimates were greater than estimates between BWT and CEH (0.82 and 0.34) or BWT and CEC (0.85 and 0.26). This result indicates that CEH and CEC should be high correlated rather than estimates between calving ease and birth weight. Genetic correlation estimates between direct genetic effects and maternal effects were -0.29, -0.31 and 0.15 for BWT, CEH and CEC, respectively. Correlation for permanent environmental effects between BWT and CEC was -0.83 in model 4. This study can provide genetic evaluation for calving ease with other continuous traits jointly with assuming that calving ease from first calving was a same trait to calving ease from later parities calving. Further researches for reliability of dispersion parameters would be needed even if the more correlated traits would be concerned in the model, the higher reliability could be obtained, especially on threshold model with property that categorical traits have little information.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1349-1364
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• 2006

An analytic approach that provides explicit estimates of risk on cataract and epilation data is evaluated by reasonableness of conceivable relative risk models regarding a simple, odds, logistic or Gompertz regression method, assuming a binomial distribution. In these analyses, we apply relative risk models with two thresholds between epilators and nonepilators from a highly characteristic lesion of which radiation cataract does not occur around 2 gray for a single acute exposure. The risk models are fitted to the data assuming 10 as a constant relative biological effectiveness of neutron. The likelihood of observing the entire data set in these models fitted is evaluated by an individual binary-response array. Estimation of a threshold with or without severe epilation and the 100 ($1-\alpha$)% confidence limits are derived from the maximum likelihood approach. The relative risk model with two thresholds can be expressed as a formula with structure of Background $\times$ RR, where RR includes threshold models with or without epilation. The radiosensitivity of ionizing radiation to cataracts has been examined for the relationship between epilators and nonepilators.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.2
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• pp.137-170
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• 2015

In this paper, we propose and analyze three viral infection models with humoral immunity including an eclipse stage of infected cells. The incidence rate of infection is represented by bilinear incidence and saturated incidence in the first and second models, respectively, while it is given by a more general function in the third one. The neutralization rate of viruses is giv0en by bilinear form in the first two models, while it is given by a general function in the third one. For each model, we have derived two threshold parameters, the basic infection reproduction number which determines whether or not a chronic-infection can be established without humoral immunity and the humoral immune response activation number which determines whether or not a chronic-infection can be established with humoral immunity. By constructing suitable Lyapunov functions we have proven the global asymptotic stability of all equilibria of the models. For the third model, we have established a set of conditions on the threshold parameters and on the general functions which are sufficient for the global stability of the equilibria of the model. We have performed some numerical simulations for the third model with specific forms of the incidence and neutralization rates and have shown that the numerical results are consistent with the theoretical results.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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• pp.1088-1097
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• 2005

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.347-356
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• 2022

This article is concerned with asymmetric volatility models for financial time series. A generalization of standard single-threshold volatility model is discussed via multiple-threshold in which we specialize to twothreshold case for ease of presentation. An empirical illustration is made by analyzing S&P500 data from NYSE (New York Stock Exchange). For comparison measures between competing models, parametric bootstrap method is used to generate forecast distributions from which summary statistics of CP (Coverage Probability) and PE (Prediction Error) are obtained. It is demonstrated that our suggestion is useful in the field of asymmetric volatility analysis.