0000000000280305

AUTHOR

Jose A. Alvarez-jareño

0000-0002-0865-4274

showing 3 related works from this author

Dynamic graphics in Excel for teaching statistics: understanding the probability density function

2011

In this article, we show a dynamic graphic in Excel that is used to introduce an important concept in our subject, Statistics I: the probability density function. This interactive graphic seeks to facilitate conceptual understanding of the main aspects analysed by the learners.

Computer graphicsGeneral MathematicsStatisticsMathematics educationSubject (documents)Probability density functionGraphicsMathematics instructionEducationTeaching Mathematics and its Applications
researchProduct

The formation of aggregate expectations: wisdom of the crowds or media influence?

2017

ABSTRACTThe general elections of 2015 in Spain were elections of change. Two new parties for which voters had no previous historical reference points burst onto the parliamentary scene. Two (partially) opposed theories vie to offer an explanation as to how voters build their aggregate electoral expectations. In this paper, we investigate which mechanism has the greatest influence on the formation of expectations: published opinion or social interactions. Likewise, we also study if there is an ideological bias in the voters’ perception of the future results of the electoral battle. Based on analysis of microdata from a survey (sample size = 14,262) conducted in Spain on the occasion of the g…

Historybusiness.industrymedia_common.quotation_subject05 social sciences050401 social sciences methodsGeneral Social SciencesSample (statistics)Public opinion0506 political scienceCrowds0504 sociologyVotingMicrodata (HTML)General election050602 political science & public administrationEconomicsIdeologyPositive economicsbusinessSocial psychologymedia_commonMass mediaContemporary Social Science
researchProduct

Combining Benford's Law and machine learning to detect money laundering. An actual Spanish court case.

2017

Abstract Objectives This paper is based on the analysis of the database of operations from a macro-case on money laundering orchestrated between a core company and a group of its suppliers, 26 of which had already been identified by the police as fraudulent companies. In the face of a well-founded suspicion that more companies have perpetrated criminal acts and in order to make better use of what are very limited police resources, we aim to construct a tool to detect money laundering criminals. Methods We combine Benford’s Law and machine learning algorithms (logistic regression, decision trees, neural networks, and random forests) to find patterns of money laundering criminals in the conte…

Actuarial scienceScrutinyArtificial neural networkComputer sciencebusiness.industryDecision treeContext (language use)02 engineering and technologySpace (commercial competition)Money launderingComputer securitycomputer.software_genreMachine learning01 natural sciencesPathology and Forensic MedicineBenford's law010104 statistics & probabilityOrder (business)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligence0101 mathematicsbusinessLawcomputerForensic science international
researchProduct