BUCEADOR, a multi-language search engine for digital libraries

This paper presents a web-based multimedia search engine built within the Buceador (www.buceador.org) research project. A proof-of-concept tool has been implemented which is able to retrieve information from a digital library made of multimedia documents in the 4 official languages in Spain (Spanish, Basque, Catalan and Galician). The retrieved documents are presented in the user language after translation and dubbing (the four previous languages + English). The paper presents the tool functionality, the architecture, the digital library and provide some information about the technology involved in the fields of automatic speech recognition, statistical machine translation, text-to-speech synthesis and information retrieval. Each technology has been adapted to the purposes of the presented tool as well as to interact with the rest of the technologies involved.Peer ReviewedPostprint (published version

Empirical Implementation of Nonparametric First-Price Auction Models

Nonparametric estimators provide a flexible means of uncovering salient features of auction data. Although these estimators are popular in the literature, many key features necessary for proper implementation have yet to be uncovered. Here we provide several suggestions for nonparamteric estimation of first-price auction models. Specifically, we show how to impose monotonicity of the equilibrium bidding strategy; a key property of structural auction models not guaranteed in standard nonparametric estimation. We further develop methods for automatic bandwidth selection. Finally, we discuss how to impose monotonicity in auctions with differering number of bidders, reserve prices, and auction-specific characteristics. Finite sample performance is examined using simulated data as well as experimental auction data.

Imposing Monotonicity Nonparametrically in First-Price Auctions

Monotonicity of the equilibrium bidding strategy is a key property of structural auction models. Traditional nonparametric estimators provide a flexible means of uncovering salient features of auction data, but do not formally impose the monotonicity assumption that is inherent in the models during estimation. Here, we develop a nonparametric estimator which imposes the monotonicity assumption. We accomplish this by employing the constraint weighted bootstrapping theory developed in the statistics literature. The finite sample performance of our estimator is examined using simulated data, experimental data, as well as a naturally occurring data set composed of thousands of bids from Canadian timber auctions

Imposing Monotonicity Nonparametrically in First-Price Auctions

Monotonicity of the equilibrium bidding strategy is a key property of structural auction models. Traditional nonparametric estimators provide a flexible means of uncovering salient features of auction data, but do not formally impose the monotonicity assumption that is inherent in the models during estimation. Here, we develop a nonparametric estimator which imposes the monotonicity assumption. We accomplish this by employing the constraint weighted bootstrapping theory developed in the statistics literature. The finite sample performance of our estimator is examined using simulated data, experimental data, as well as a naturally occurring data set composed of thousands of bids from Canadian timber auctions