This paper outlines a complete bootstrapping approach to the estimation and analysis of macroeconometric models. It combines the bootstrapping literature initiated by Efron (1979) and the stochastic simulation literature initiated by Adelman and Adelman (1959).

According to amuch discussed argument, reliabilism is defective formaking knowledge too easy to come by. In a recent paper, Weisberg aims to show that this argument relies on a type of reasoning that is rejectable on independent grounds. We argue that the blanket rejection that Weisberg recommends of this type of reasoning is both unwarranted and unwelcome. Drawing on an older discussion in the...

Children use syntax to guide verb learning in a process known as syntactic bootstrapping. Recent work explores how syntactic bootstrapping works-how it begins, and how it interacts with progress in syntax acquisition. We review evidence for three claims about the mechanisms and representations underlying syntactic bootstrapping: (1) Learners are biased to represent linguistic knowledge in a use...

It is known that bootstrapping maximum for estimating the endpoint of a distribution function is inconsistent and subsample bootstrap method is needed. Under an extreme value condition, some other estimators for the endpoint have been studied in the literature, which are preferrable to the maximum in regular cases. In this paper, we show that the full sample bootstrap method is consistent for t...

Seed sampling is critical in semi-supervised learning. This paper proposes a clusteringbased stratified seed sampling approach to semi-supervised learning. First, various clustering algorithms are explored to partition the unlabeled instances into different strata with each stratum represented by a center. Then, diversity-motivated intra-stratum sampling is adopted to choose the center and addi...

Susan Carey's account of Quinean bootstrapping has been heavily criticized. While it purports to explain how important new concepts are learned, many commentators complain that it is unclear just what bootstrapping is supposed to be or how it is supposed to work. Others allege that bootstrapping falls prey to the circularity challenge: it cannot explain how new concepts are learned without pres...

Bootstrapping has recently become the focus of much attention in natural language processing to reduce labeling cost. In bootstrapping, unlabeled instances can be harvested from the initial labeled “seed” set. The selected seed set affects accuracy, but how to select a good seed set is not yet clear. Thus, an “iterative seeding” framework is proposed for bootstrapping to reduce its labeling cos...

Distributed Hash Table (DHT)-based protocols are new approaches proposed to Wireless Sensor Networks (WSN). Their main advantage resides on the easy integration of DHT-based WSN into the Internet Of Things. However, these protocols face multiple challenges in their bootstrapping phase, especially at the case of randomly deployed WSN. We presented in a recent work a bootstrapping protocol for us...

We introduce a general technique for making statistical inference from clustering tools applied to gene expression microarray data. The approach utilizes an analysis of variance model to achieve normalization and estimate differential expression of genes across multiple conditions. Statistical inference is based on the application of a randomization technique, bootstrapping. Bootstrapping has p...

We present a semi-supervised (bootstrapping) approach to the extraction of time expression mentions in large unlabelled corpora. Because the only supervision is in the form of seed examples, it becomes necessary to resort to heuristics to rank and filter out spurious patterns and candidate time expressions. The application of bootstrapping to time expression recognition is, to the best of our k...