Browsing by Author "Lee, Jack C."

Now showing 1 - 14 of 14

A semiparametric method for predicting bankruptcy
(2007-08-01)
Hwang, Ruey-Ching
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Cheng, K. F.
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Lee, Jack C.
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管理科學系
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資訊管理與財務金融系註：原資管所+財金所
Bankruptcy prediction methods based on a semiparametric logit model are proposed for simple random (prospective) and case-control (choice-based; retrospective) data. The unknown parameters and prediction probabilities in the model are estimated by the local likelihood approach, and the resulting estimators are analyzed through their asymptotic biases and variances. The semiparametric bankruptcy prediction methods using these two types of data are shown to be essentially equivalent. Thus our proposed prediction model can be directly applied to data sampled from the two important designs. One real data example and simulations confirm that our prediction method is more powerful than alternatives, in the sense of yielding smaller out-of-sample error rates. Copyright (C) 2007 John Wiley & Sons, Ltd.
A Universal Model for Interface-type Threshold Switching Phenomena by Comprehensive Study of Vanadium Oxide-Based Selector
(2017-01-01)
Lin, Chih-Yang
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Chen, Ying-Chen
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Gu, Meiqi
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Pan, Chih-Hung
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Jin, Fu-Yuan
For the first time, a comprehensive study of Vanadium oxide-based selector characteristics with a universal model observed by thermal and electrical induced threshold switching (TS) phenomena at interface is presented in this work. The model can explain that the resistance evolution by thermal temperature in TS behaviors, as well as the resistance gradually increases with cycling ("seasoning effect"). Compatible current density (10(7)similar to 10(9) A/cm(2)) and selectivity (similar to 100) with physical understanding of evolution in energy barrier and MIT metallic state modulation are studied. The results show a promising design guideline for future storage-class memory (SCM) applications.
Bayesian analysis of hierarchical linear mixed modeling using the multivariate t distribution
(2007-02-01)
Lin, Tsung I.
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Lee, Jack C.
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統計學研究所
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資訊管理與財務金融系註：原資管所+財金所
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Institute of Statistics
This article presents a fully Bayesian approach to modeling incomplete longitudinal data using the t linear mixed model with AR(p) dependence. Markov chain Monte Carlo (MCMC) techniques are implemented for computing posterior distributions of parameters. To facilitate the computation, two types of auxiliary indicator matrices are incorporated into the model. Meanwhile, the constraints on the parameter space arising from the stationarity conditions for the autoregressive parameters are handled by a reparametrization scheme. Bayesian predictive inferences for the future vector are also investigated. An application is illustrated through a real example from a multiple sclerosis clinical trial. (c) 2006 Elsevier B.V. All rights reserved.
Comprehensive Trap-Level Study in SiOx-based Resistive Switching Memory
(2013)
Chang, Yao-Feng
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Chen, Ying-Chen
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Li, Ji
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Xue, Fei
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Wang, Yanzhen
Estimation and prediction in linear mixed models with skew-normal random effects for longitudinal data
(2008-04-30)
Lin, Tsung I.
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Lee, Jack C.
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
This paper extends the classical linear mixed model by considering a multivariate skew-normal assumption for the distribution of random effects. We present an efficient hybrid ECME-NR algorithm for the computation of maximum-likelihood estimates of parameters. A score test statistic for testing the existence of skewness preference among random effects is developed. The technique for the prediction of future responses under this model is also investigated. The methodology is illustrated through an application to Framingham cholesterol data and a simulation study. Copyright (C) 2007 John Wiley & Sons, Ltd.
Estimation of a utility-based asset pricing model using normal mixture GARCH(1,1)
(2007-03-01)
Wu, C. C.
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Lee, Jack C.
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統計學研究所
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管理科學系
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資訊管理與財務金融系註：原資管所+財金所
Brown and Gibbons [Brown, D.P., Gibbons, MR., 1985. A simple econometric approach for utility-based asset pricing model. Journal of Finance 40, 359-381], Karson et al. [Karson, M., Cheng, D., Lee, C. F., 1995. Sampling distribution of the relative risk aversion estimator: theory and applications. Review of Quantitative Finance and Accounting 5, 43-54], and Lee et al. [Lee, C.F., Lee, J.C., Ni, H.F., Wu, C.C., 2004. On a simple econometric approach for utility-based asset pricing model. Review of Quantitative Finance and Accounting 22, 331-344] developed the theory and the distribution of unconditional relative risk aversion (RRA) estimates in utility-based asset pricing model by assuming normality for the log excess returns. While the normality assumption is not always appropriate for some security returns, Brown and Gibbons [Brown, D.P., Gibbons, M.R., 1985. A simple econometric approach for utility-based asset pricing model. Journal of Finance 40, 359-381] proposed generalized method of moments (GMM) to estimate unconditional RRA. However, RRA estimated by GMM is not statistically efficient with finite samples. The main purpose of this paper is to derive the process of estimating dynamic RRA with the maximum likelihood and a Bayesian method having a weakly informative prior density while assuming that the log excess returns on the market are distributed as normal mixture GARCH(1,1). This methodology will capture the variations of RRA across different periods. Empirical results are presented using market rates of returns and risk-free rates over the period 1941 to 2001. (c) 2006 Elsevier B.V. All rights reserved.
Finite mixture modelling using the skew normal distribution
(2007-07-01)
Lin, Tsung I.
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Lee, Jack C.
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Yen, Shu Y.
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統計學研究所
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資訊管理與財務金融系註：原資管所+財金所
Normal mixture models provide the most popular framework for modelling heterogeneity in a population with continuous outcomes arising in a variety of subclasses. In the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied problems. In this article, we address the problem of analyzing a mixture of skew normal distributions from the likelihood-based and Bayesian perspectives, respectively. Computational techniques using EM-type algorithms are employed for iteratively computing maximum likelihood estimates. Also, a fully Bayesian approach using the Markov chain Monte Carlo method is developed to carry out posterior analyses. Numerical results are illustrated through two examples.
Forecasting Time-Varying Covariance with a Robust Bayesian Threshold Model
(2011-08-01)
Wu, Chih-Chiang
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Lee, Jack C.
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
This paper proposes a robust multivariate threshold vector autoregressive model with generalized autoregressive conditional heteroskedasticities and dynamic conditional correlations to describe conditional mean, volatility and correlation asymmetries in financial markets. In addition, the threshold variable for regime switching is formulated as a weighted average of endogenous variables to eliminate excessively subjective belief in the threshold variable decision and to serve as the proxy in deciding which market should be the price leader. The estimation is performed using Markov chain Monte Carlo methods. Furthermore, several meaningful criteria are introduced to assess the forecasting performance in the conditional covariance matrix. The proposed methodology is illustrated using daily S&P500 futures and spot prices. Copyright (C) 2010 John Wiley & Sons, Ltd.
Generalized inferences on the common mean vector of several multivariate normal populations
(2007-07-01)
Lin, S. H.
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Lee, Jack C.
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Wang, R. S.
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統計學研究所
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資訊管理與財務金融系註：原資管所+財金所
The hypothesis testing and confidence region are considered for the common mean vector of several multivariate normal populations when the covariance matrices are unknown and possibly unequal. A generalized confidence region is derived using the concepts of generalized method based on the generalized p-value. The generalized confidence region is illustrated with two numerical examples. The merits of the proposed method are numerically compared with those of existing methods with respect to their expected area or expected d-dimensional volumes and coverage probabilities under different scenarios. (c) 2006 Elsevier B.V. All rights reserved.
Integrated One Diode-One Resistor Architecture in Nanopillar SiOx Resistive Switching Memory by Nanosphere Lithography
(2014-02-01)
Ji, Li
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Chang, Yao-Feng
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Fowler, Burt
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Chen, Ying-Chen
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Tsai, Tsung-Ming
We report on a highly compact, one diode-one resistor (1D-1R) nanopillar device architecture for SiOx-based ReRAM fabricated using nanosphere lithography (NSL). The intrinsic SiOx-based resistive switching element and Si diode are self-aligned on an epitaxial silicon wafer using NSL and a deep-Si-etch process without conventional photolithography. AC-pulse response in 50 ns regime, multibit operation, and good reliability are demonstrated. The NSL process provides a fast and economical approach to large-scale patterning of high-density 1D-1R ReRAM with good potential for use in future applications.
On the distribution of the inverted linear compound of dependent F-variates and its application to the combination of forecasts
(2006-11-01)
Liang, Kuo-Yuan
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Lee, Jack C.
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Shao, Kurt S. H.
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交大名義發表
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資訊管理與財務金融系註：原資管所+財金所
This paper establishes a sampling theory for an inverted linear combination of two dependent F-variates. It is found that the random variable is approximately expressible in terms of a mixture of weighted beta distributions. Operational results, including rth-order raw moments and critical values of the density are subsequently obtained by using the Pearson Type I approximation technique. As a contribution to the probability theory, our findings extend Lee & Hu's (1996) recent investigation on the distribution of the linear compound of two independent F-variates. In terms of relevant applied works, our results refine Dickinson's (1973) inquiry on the distribution of the optimal combining weights estimates based on combining two independent rival forecasts, and provide a further advancement to the general case of combining three independent competing forecasts. Accordingly, our conclusions give a new perception of constructing the confidence intervals for the optimal combining weights estimates studied in the literature of the linear combination of forecasts.
Resistive Switching of SiOX with One Diode-One Resistor Nanopillar Architecture Fabricated via Nanosphere Lithography
(2014)
Ji, Li
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Chang, Yao-Feng
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Fowler, Burt
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Chen, Ying-Chen
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Tsai, Tsung-Ming
Robust mixture modeling using the skew t distribution
(2007-06-01)
Lin, Tsung I.
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Lee, Jack C.
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Hsieh, Wan J.
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統計學研究所
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資訊管理與財務金融系註：原資管所+財金所
A finite mixture model using the Student's t distribution has been recognized as a robust extension of normal mixtures. Recently, a mixture of skew normal distributions has been found to be effective in the treatment of heterogeneous data involving asymmetric behaviors across subclasses. In this article, we propose a robust mixture framework based on the skew t distribution to efficiently deal with heavy-tailedness, extra skewness and multimodality in a wide range of settings. Statistical mixture modeling based on normal, Student's t and skew normal distributions can be viewed as special cases of the skew t mixture model. We present analytically simple EM-type algorithms for iteratively computing maximum likelihood estimates. The proposed methodology is illustrated by analyzing a real data example.
The Voltage-Triggered SET Mechanism and Self-Compliance Characteristics in Intrinsic Unipolar SiOx-Based Resistive Switching Memory
(2014)
Chang, Yao-Feng
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Fowler, Burt
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Chen, Ying-Chen
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Ji, Li
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Zhou, Fei