Bootstrapping Analysis of Lifetime Data with Subsampling

Because of cost and time limitations, reliability experiments frequently contain subsampling, which is a restriction on randomization. A two‐stage approach can analyze right censored Weibull distributed reliability data with subsampling. However, in implementing such a method, we found that it did not address the problems of how to perform confidence intervals of low percentiles and reduce the bias of estimates. In this paper, we present a two‐stage bootstrapping approach and an unbiasing factor approach to solve the aforementioned problems. An example is provided to illustrate the proposed method. In addition, the proposed method is compared with existing methods through simulation. The resulting simulations show that the proposed method performs well in low percentiles. Copyright © 2015 John Wiley & Sons, Ltd.     

Data envelopment analysis with classification and regression tree – a case of banking efficiency

Abstract: Data envelopment analysis (DEA) is a non‐parametric method for measuring the efficiency and productivity of decision‐making units (DMUs). On the other hand data mining techniques allow DMUs to explore and discover meaningful, previously hidden information from large databases. Classification and regression (C&R) is the commonly used decision tree in data mining. DEA determines the efficiency scores but cannot give details of factors related to inefficiency, especially if these factors are in the form of non‐numeric variables such as operational style in the banking sector. This paper proposes a framework to combine DEA with C&R for assessing the efficiency and productivity of DMUs. The result of the combined model is a set of rules that can be used by policy makers to discover reasons behind efficient and inefficient DMUs. As a case study, we use the proposed methodology to investigate factors associated with the efficiency of the banking sector in the Gulf Cooperation Council countries.     

一个半监督的中文事件抽取方法

半监督或无监督的事件抽取方法在目前依旧是一个具有挑战性的课题。针对中文本身在表述中存在的固有特点,该文提出一种基于双视图的事件抽取自举学习方法。该方法以少量种子为基础,从文档相关度与语义相似度两个视图出发,进行交互过滤筛选,不断抽取新的有效事件模板,为事件抽取服务。在ACE2005中文语料上的测试表明,和现有方法相比,该方法可以有效地提高中文信息事件抽取系统的性能。     

Sufficient bootstrapping

In this paper, we introduce an idea we refer to as sufficient bootstrapping, which is based on retaining only distinct individual responses, and also develop a theoretical framework for the techniques. We demonstrate through numerical illustrations that the proposed sufficient bootstrapping may be better than the conventional bootstrapping in certain situations. The expected gain by the sufficient bootstrapping has been computed for small and large sample sizes. The relative efficiency shows that there could be significant gain by the sufficient bootstrapping and it could reduce computational burden. Variance expressions for both the conventional and sufficient bootstrapping sample means are derived. Here the word “sufficient” is being used in the sense that it is “sufficient to take just one of any duplicated items in the bootstrap sample” and is not tightly connected to sufficiency in terms of any likelihood perspective. R code for comparing bootstrapping and sufficient bootstrapping are provided. A huge scope of further studies is suggested.     

Disambiguation of partial cognates

Partial cognates are pairs of words in two languages that have the same meaning in some, but not all contexts. Detecting the actual meaning of a partial cognate in context can be useful for Machine Translation tools and for Computer-Assisted Language Learning tools. We propose a supervised and a semi-supervised method to disambiguate partial cognates between two languages: French and English. The methods use only automatically-labeled data; therefore they can be applied to other pairs of languages as well. The aim of our work is to automatically detect the meaning of a French partial cognate word in a specific context.

A self‐applicable online partial evaluator for recursive flowchart languages

This paper describes a self‐applicable online partial evaluator for a flowchart language with recursive calls. Self‐application of the partial evaluator yields generating extensions that are as efficient as those reported in the literature for offline partial evaluation. This result is remarkable because it has been assumed that online partial evaluation techniques unavoidably lead to inefficient and overgeneralized generating extensions. The purpose of this paper is not to determine which kind of partial evaluation is better, but to show how the problem can be solved by recursive polyvariant specialization. The design of the self‐applicable online partial evaluator is based on a number of known techniques, but by combining them in a new way this result can be produced. The partial evaluator, its techniques, and its implementation are presented in full. Self‐application according to all three Futamura projections is demonstrated. The complete bootstrap of a compiler generator from a partial evaluator is also reported. Copyright © 2011 John Wiley & Sons, Ltd.     

Bootstrapping a weighted linear estimator of the ARCH parameters

Abstract.  A standard assumption while deriving the asymptotic distribution of the quasi maximum likelihood estimator in ARCH models is that all ARCH parameters must be strictly positive. This assumption is also crucial in deriving the limit distribution of appropriate linear estimators (LE). We propose a weighted linear estimator (WLE) of the ARCH parameters in the classical ARCH model and show that its limit distribution is multivariate normal even when some of the ARCH coefficients are zero. The asymptotic dispersion matrix involves unknown quantities. We consider appropriate bootstrapped version of this WLE and prove that it is asymptotically valid in the sense that the bootstrapped distribution (given the data) is a consistent estimate (in probability) of the distribution of the WLE. Although we do not show theoretically that the bootstrap outperforms the normal approximation, our simulations demonstrate that it yields better approximations than the limiting normal.     

Language Simplification through Error-Correcting and Grammatical Inference Techniques

In many language processing tasks, most of the sentences generally convey rather simple meanings. Moreover, these tasks have a limited semantic domain that can be properly covered with a simple lexicon and a restricted syntax. Nevertheless, casual users are by no means expected to comply with any kind of formal syntactic restrictions due to the inherent spontaneous nature of human language. In this work, the use of error-correcting-based learning techniques is proposed to cope with the complex syntactic variability which is generally exhibited by natural language. In our approach, a complex task is modeled in terms of a basic finite state model, F, and a stochastic error model, E. F should account for the basic (syntactic) structures underlying this task, which would convey the meaning. E should account for general vocabulary variations, word disappearance, superfluous words, and so on. Each natural user sentence is thus considered as a corrupted version (according to E) of some simple sentence of L(F). Adequate bootstrapping procedures are presented that incrementally improve the structure of F while estimating the probabilities for the operations of E. These techniques have been applied to a practical task of moderately high syntactic variability, and the results which show the potential of the proposed approach are presented.     

一种用于高速高精度A/D转换器的时钟自举电路

设计了一种双电容结构时钟自举电路,分析了电路工作原理,用Cadence Spectre仿真器和0.35μm CMOS PDK进行电路前仿真和后仿真.仿真结果表明,设计的双电容结构时钟自举电路能使采样电路线性度达到110dB以上,该电路已用于16位A/D转换器的设计并流片.经测试,采用该结构的16位A/D转换器的SFDR为96.25dB(FS),信噪比为76.45dB(FS).     

Bootstrapping conjectural indicators of vulnerability for schizophrenia: A Reply to Faraone's critique of Watt, Grubb, and Erlenmeyer-Kimling.

Presented is a reply to the article "Validating indicators of vulnerability to schizophrenia: A comment on Watt, Grubb, and Erlenmeyer- Kimling", (1983). Demonstration of statistically significant mean differences between high-risk and normal control children provides little information concerning the validity and clinical utility of conjectural indicators of vulnerability to schizophrenia. Taxometric methods offer a promising approach for preliminary testing of the validity and clinical utility of such indicators. It is not required that the indicators be nearly infallible. Accurate identification of vulnerability is still possible by combining several indicators that are fallible but relatively independent within the vulnerable and nonvulnerable subgroups. Mixed-group validation methods would be preferable to criterion-group methods in some vulnerability studies but they require dependable estimates of the base rates of vulnerability in high-risk and control groups, which are not available in the present instance. Because of the lack of an available criterion measure of vulnerability in high-risk studies, uncertainty about the validity of conjectural indicators is presently unavoidable. (PsycINFO Database Record (c) 2010 APA, all rights reserved)