對於現時狀態的數據(current status data)，真正的失效時間(failure time)可能無法直接被觀察到，這一類型的資料，我們所能得到的資訊是檢測時間(monitoring time)以及失效時間是發生在檢測時間之前或是之後。因此，若想從這一類型的資料中獲得更多的資訊，則檢測時間是非常重要的。在這篇論文中，我們經由實驗設計的方法，根據D-與Ds-之最適設計準則，去尋找最佳的檢測時
間Ci (i =1, … ,n)使得我們在二維的關聯模型之下，可以獲得最多的資訊。然後在Clayton 關聯模型之下，經由模擬分析，所得之模擬數據，再根據這些最佳檢測時間*
D C 和*Ds C ，藉由最大概似函數(maximum likelihood function)估計法，去同時估計這些未知參數的MLE，並比較在D-與Ds-之最適設計下的表現。

For current status data, the failure time of interest may not be observed exactly. The type of this data consists only of a monitoring time and knowledge of whether the failure time occurred before or after the monitoring time. In order to be able to obtain more information from this data, so the monitoring time is very important. In this work, the optimal designs for determining the monitoring times such that maximum information may be obtained in bivariate copula model (Clayton) are investigated. Here, the D-
optimal criterion is used to decide the best monitoring time Ci (i = 1; ¢ ¢ ¢ ; n), then use these monitoring times Ci to estimate the unknown parameters simultaneously by maximizing the corresponding likelihood function. Ds-optimal designs for estimation
of association parameter in the copula model are also discussed. Simulation studies are presented to compare the performance of using monitoring time C¤D and C¤Ds to do the estimation.

Abstract
List of Tables
List of Figures
1 Introduction
2 Preliminaries
2.1 Linear model
2.2 Generalized linear models
2.3 Copula models
2.4 Inferences
3 D- and Ds-optimal designs
3.1 Under independence
3.2 Under association
4 Numerical simulation
4.1 Generating the bivariate current status data
based on a copula model
4.2 Simulation Studies
5 Conclusion
References
Appendix

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