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Katz and King have previously developed a model for predictingor explaining aggregate electoral results in multiparty democracies.Their model is, in principle, analogous to what least-squaresregression provides American political researchers in that two-partysystem. Katz and King applied their model to three-party electionsin England and revealed a variety of new features of incumbencyadvantage and sources of party support. Although the mathematicsof their statistical model covers any number of political parties,it is computationally demanding, and hence slow and numericallyimprecise, with more than three parties. In this paper we producean approximate method that works in practice with many partieswithout making too many theoretical compromises. Our approachis to treat the problem as one of missing data. This allowsus to use a modification of the fast EMis algorithm of King,Honaker, Joseph, and Scheve and to provide easy-to-use software,while retaining the attractive features of the Katz and Kingmodel, such as the t distribution and explicit models for uncontestedseats. 相似文献
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This is a second in a series of two articles in which I challengethe collective administration of performing rights. In the firstarticle, published in a recent issue of this journal, I questionedthe natural monopoly paradigm that dominates the analysis ofcollective administration of performing rights. In this articleI demonstrate how, by lowering many of the transaction costswhich previously purported to justify the practice, new digitaltechnologies further undermine the justification for collectiveadministration. I also discuss whether market forces alone wouldtransform the market into a competitive one, consider possiblecontinuing roles for existing performing rights organizations,and compare the Canadian and the U.S. regulatory approachesto determine how conducive they are to such change. 相似文献
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Mark Guido M.S. Marc Brooks M.S. M.B.A. Justin Grover M.S. Eric Katz M.S. Jared Ondricek M.S. Marcus Rogers Ph.D. Lauren Sharpe M.S. 《Journal of forensic sciences》2016,61(6):1467-1472
The Periodic Mobile Forensics (PMF) system investigates user behavior on mobile devices. It applies forensic techniques to an enterprise mobile infrastructure, utilizing an on‐device agent named TractorBeam. The agent collects changed storage locations for later acquisition, reconstruction, and analysis. TractorBeam provides its data to an enterprise infrastructure that consists of a cloud‐based queuing service, relational database, and analytical framework for running forensic processes. During a 3‐month experiment with Purdue University, TractorBeam was utilized in a simulated operational setting across 34 users to evaluate techniques to identify masquerading users (i.e., users other than the intended device user). The research team surmises that all masqueraders are undesirable to an enterprise, even when a masquerader lacks malicious intent. The PMF system reconstructed 821 forensic images, extracted one million audit events, and accurately detected masqueraders. Evaluation revealed that developed methods reduced storage requirements 50‐fold. This paper describes the PMF architecture, performance of TractorBeam throughout the protocol, and results of the masquerading user analysis. 相似文献