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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/33887
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/33887


    Title: Dichotomous-Data Reliability Models with Auxiliary Measurements
    Authors: 俞一唐
    Yu, I-Tang
    Contributors: 傅承德
    余清祥

    Fuh, Cheng-Der
    Yue, Ching-Syang

    俞一唐
    Yu, I-Tang
    Keywords: 拔靴法
    衰變量
    二元資料
    電火工品
    EM演算法
    bootstrap method
    degradation measurement
    dichotomous data
    electro-explosive device
    EM-algorithm
    latent variables
    Markov Chain Monte Carlo
    reliability
    Date: 2003
    Issue Date: 2009-09-17 18:43:54 (UTC+8)
    Abstract: 我們提供一個新的可靠度模型,DwACM,並提供一個模式選擇準則CCP,我們利用DwACM和CCP來選擇衰變量。
    We propose a new reliability model, DwACM (Dichotomous-data with Auxiliary Continuous Measurements model) to describe a data set which consists of classical dichotomous response (Go or No Go) associated with a set of continuous auxiliary measurement. In this model, the lifetime of each individual is considered as a latent variable. Given the value of the latent variable, the dichotomous response is either 0 or 1
    depending on if it fails or not at the measuring time. The continuous measurement can be regarded as observations of an underlying possible degradation candidate of which descending process is a function of the lifetime. Under the assumption that the failure of products is defined as the time at which the
    continuous measurement reaches a threshold, these two measurements can be linked in the proposed model. Statistical inference under this model are both in frequentist and Bayesian frameworks. To evaluate the continuous measurements, we provide a criterion, CCP (correct classification probability),
    to select the best degradation measurement. We also report our
    simulation studies of the performances of parameters estimators and CCP.
    Reference: 1.Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete
    data via the EM algorithm (with discussion). Journal of the Royal Statistical Society
    B, 39, 1-38.
    2. Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap.
    Chapman \& Hall, Inc., London.
    3. Gelfand, A. E. and Smith A. F. M. (1990). Sampling based approaches to calculating
    marginal densities.
    Journal of the American Statistical Association 85, 398-409.
    4.Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs
    distribution and the Bayesian restoration of images. IEEE
    Trans. Pattn. Anal. Math. Intel., 6, 721-741.
    5. Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (1996).
    Markov Chain Monte Carlo in Practice. Chapman \& Hall/CRC, London.
    6.Lawless, J. F. (1982). Statistical Models and Methods for Lifetime Data.
    John Wiley \& Sons, New York.
    7.. Hall, P. (1992). The Bootstrap and Edgeworth Expansion.
    New York: Springer-Verlag.
    8. Hastings, W. K. (1970). Monte carlo sampling methods using
    Markov chains and their applications. Biometrika, 57,
    97-109.
    9. Hudak, S. J. Jr., Saxena, A., Bussi, R. J. and Malcolm, R.
    C. (1978). Development of standard methods of testing and analyzing
    fatigue crack growth rate data. Technical Report AFML-TR-78-40
    Westinghouse R \& D Center, Westinghouse Electric Corporation,
    Pittsburgh, PA 15235.
    10. Lu, C. J. and Meeker, W. Q. (1993). Using degradation measures to estimate a time-to-failure distribution.
    Technometrics, 35, 161-174.
    11. McLachlan, G. J. and Krishnan, T. (1997). The EM Algorithm and Extensions.
    John Wiley \& Sons, New York.
    12. Meeker, W. Q. and Escobar, L. A. (1998). Statistical Methods for Reliability Data.
    John Wiley \& Sons, New York.
    13. Meng, X. L. and Rubin, D. B. (1993). Maximum likelihood estimation via the ECM algorithm
    : a general framework.Biometrika B, 80, 267-278.
    14. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N.,
    Teller, A. H. and Teller, E (1953). Equations of state calculations
    by fast computing machine. J. Chem. Phys, 21,
    1087-1091.
    15. Murphy, A, J. and Menichelli, V. J. (1979). Introduction to thermal transient testing.
    Technical report, Pasadena Scientific Industries.
    16. Sammel, M. D., Ryan, L. M. and Legler, J. M. (1997). Latent variable models for mixed
    discrete and continuous outcomes. Journal of the Royal Statistical Society
    B, 59, 667-678.
    17. Taguchi, G. (1991).Taguchi Methods, Signal-to-Noise Ratio for Quality Evaluation}, Vol 3.
    Dearborn, MI: American Supplier Institute Press.
    18.Tierney, L. (1994). Markov chains for exploring posterior
    distributions (with discussion). Ann. Statist, 22,
    1701-1762.
    19. Tseng, S. T., Hamada, M. and Chiao, C. H. (1995). Using degradation data from a factorial
    experiment to improve fluorescent lamp reliability. Journal of Quality Technology
    46, 130-133.
    20. Wei, G. C. G. and Tanner, M. A. (1990). A Monte Carlo implementation of the EM algorithm
    and the poor man's data augmentation algorithms.
    Journal of the American Statistical Association 85, 699-704.
    21.Wu, C. F. J. and Hamada, M. (2000). Experiments Planning, Analysis, and Parameter Design
    Optimization. John Wiley \& Sons, New York.
    Description: 國立政治大學
    統計研究所
    86354503
    92
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0086354503
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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