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Robust Estimators are Hard to Compute
Bernholt, Thorsten - 2006
In modern statistics, the robust estimation of parameters of a regression hyperplane is a central problem. Robustness means that the estimation is not or only slightly affected by outliers in the data. In this paper, it is shown that the following robust estimators are hard to compute: LMS, LQS,...
Persistent link: https://www.econbiz.de/10010296716
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Stochastic Dominance Efficiency Analysis of Diversified Portfolios: Classification, Comparison and Refinements
Lizyayev, Andrey - 2010
For more than three decades, empirical analysis of stochastic dominance was restricted to settings with mutually exclusive choice alternatives. In recent years, a number of methods for testing efficiency of diversified portfolios have emerged, which can be classified into three main categories:...
Persistent link: https://www.econbiz.de/10010325987
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A note on estimating censored quantile regressions
Fitzenberger, Bernd - 1994
This note is concerned with estimating censored quantile regressions (CQR). As its major contribution, a' new algorithm, called BRCENS, is developed as an adaption of the Barrodale-Roberts algorithm for the standard quantile regression problem. In a subsequent simulation study, BRCENS performs...
Persistent link: https://www.econbiz.de/10010332101
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On the initialization of adaptive learning in macroeconomic models
Berardi, Michele; Galimberti, Jaqueson K. - 2016
We review and evaluate methods previously adopted in the applied literature of adaptive learning in order to initialize agents' beliefs. Previous methods are classified into three broad classes: equilibrium-related, training sample-based, and estimation-based. We conduct several simulations...
Persistent link: https://www.econbiz.de/10011582421
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Cuckoo search via Levy flights applied to uncapacitated facility location problem
Mesa, Armacheska; Castromayor, Kris; … - In: Journal of Industrial Engineering International 14 (2018) 3, pp. 585-592
swarm optimization and other existing algorithms. Although these results showed that CS-LF is a promising method to solve …
Persistent link: https://www.econbiz.de/10011995856
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Analyzing continuance intention of recommendation algorithms
Kim, Jiwhan; Nam, Changi - 2019
As recommendation algorithms have been increasingly applied to content personalization services, scholars are voicing … concern about the negative impacts of these algorithms, for instance filter bubbles and ideological polarization. This … research attempts to analyze the various factors influencing users' continuance intention of recommendation algorithms through …
Persistent link: https://www.econbiz.de/10012113508
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Socio-Technical Design of Algorithms: Fairness, Accountability, and Transparency
Shin, Don D.H. - 2019
Algorithms play increasingly critical roles these days. With the conceptualization of algorithms as a socio … such as privacy, transparency, and fairness emerge as critical concerns of the social ramifications of algorithms and their … impact on the new information milieu. With these rising issues, the questions are raised as to how to govern algorithms and …
Persistent link: https://www.econbiz.de/10012113530
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Mechanisms in a Digitalized World
Demange, Gabrielle - 2018
Due to computing and communication facilities, formal procedures, often referred to as ‘algorithms’, are now … discuss some of the economic risks generated by the power of algorithms. …
Persistent link: https://www.econbiz.de/10011872041
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Competition policy reform in Europe and Germany - Institutional change in the light of digitization
Budzinski, Oliver; Stöhr, Annika - 2018
The ubiquitous process of digitization changes economic competition on markets in several ways and leads to the emergence of new business models. The increasing roles of digital platforms as well as data-driven markets represent two relevant examples. These developments challenge competition...
Persistent link: https://www.econbiz.de/10011927073
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The estimation of parameters in nonlinear, implicit measurement error models with experiment-wide measurements
Anderson, K.K. - 2008
Measurement error modeling is a statistical approach to the estimation of unknown model parameters which takes into account the measurement errors in all of the data. Approaches which ignore the measurement errors in so-called independent variables may yield inferior estimates of unknown model...
Persistent link: https://www.econbiz.de/10009435461