DSpace collection: 專書之單篇

2023台灣各產業景氣趨勢調查報告_第25章數位平台經濟

Fri, 28 Apr 2023 13:15:47 GMT

title: 2023台灣各產業景氣趨勢調查報告_第25章數位平台經濟 abstract: 為因應新的經濟情勢以及各界需求，「2023台灣各產業景氣趨勢調查報告」不僅對2022年國內外重要國家總體經濟現況有詳盡報導，亦提供台灣製造業、服務業與營建業之未來總體發展願景分析；針對二十多項重要中分業於2023年產業景氣趨勢，提供各項統計數據、景氣調查與前瞻分析。此外，產業轉型發展篇以未來具有潛力之產業為主，提供產業趨勢，以及推動方向與發展策略。

Quantifying the Uncertainty in Optimal Experiment Schemes via Monte-Carlo Simulations

Sat, 18 May 2019 04:12:32 GMT

title: Quantifying the Uncertainty in Optimal Experiment Schemes via Monte-Carlo Simulations abstract: In the process of designing life-testing experiments , experimenters always establish the optimal experiment scheme based on a particular parametric lifetime model. In most applications, the true lifetime model is unknown and need to be specified for the determination of optimal experiment schemes. Misspecification of the lifetime model may lead to a substantial loss of efficiency in the statistical analysis. Moreover, the determination of the optimal experiment scheme is always relying on asymptotic statistical theory. Therefore, the optimal experiment scheme may not be optimal for finite sample cases. This chapter aims to provide a general framework to quantify the sensitivity and uncertainty of the optimal experiment scheme due to misspecification of the lifetime model. For the illustration of the methodology developed here, analytical and Monte-Carlo methods are employed to evaluate the robustness of the optimal experiment scheme for progressive Type-II censored experiment under the location-scale family of distributions.

Functional Clustering of Longitudinal Data

Mon, 30 Nov 2009 05:17:37 GMT

title: Functional Clustering of Longitudinal Data abstract: This study considers two clustering criteria to achieve difierent goals of grouping similar curves. These criteria are based on the minimal L2 distance and the maximal functional correlation defined in this study, respectively. Each cluster centers on a subspace spanned by the cluster mean and covariance eigenfunctions of the underlying random functions. Clusters can thus be identified by the subspace projection of curves.