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#SBIB:303H520 --- regressie-analyse --- wiskundige statistiek --- Methoden sociale wetenschappen: techniek van de analyse, algemeen

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Mathematical statistics --- Robust statistics. --- kwantitatieve methoden --- regressie-analyse --- lineaire programmering --- Robust statistics --- Statistics, Robust --- Distribution (Probability theory)

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The desire to have access to information at all times, while at the same time being informed automatically is the driving force behind this thesis. The goal is to bring an existing dashboard to mobile devices and to research algorithms that find outliers in timeseries.Because the dasboard is only used by a small number of people it is important to find a cheap and maintainable solution for the mobile dashboard. This has been achieved by using the HTML5 standard in combination with SVG and media queries to transform the dashboard into a webapp.When outliers are detected in specific metrics the qualified persons are alerted. However, the current solution generates too much alerts. These metrics are made of hourly measurements of activities on the products of Massive Media. With linear regression the dataset is decomposed in a trend and seasonal component. The remainder is assumed to fit a normal distribution. Outliers can then be found and reported by using a robust measurement for variation like the Median Absolute Deviation (MAD).This method finds outliers in the data that are undetectable with the naked eye and finds 40% less outliers than the currently used method. Vandaag de dag wordt er verwacht dat informatie op elk moment beschikbaar is, waarbij men tegelijkertijd proactief wordt geïnformeerd. Het doel van deze scriptie is dan ook een bestaand dashboard op mobiele toestellen brengen en algoritmen onderzoeken die uitschieters in data ontdekken.Door het beperkt publiek dat het dashboard gebruikt is het van belang een goedkope en onderhoudbare oplossing te vinden. Dit wordt bereikt door gebruik te maken van de nieuwe HTML5 standaard in combinatie met Scalable Vector Graphics (SVG) en media queries om het dashboard om te vormen tot een webapp.Wanneer uitschieters in data voorkomen worden de bevoegde personen verwittigd. De huidige oplossing genereert echter te veel alerts. De datasets bestaan uit uurgebonden metingen van activiteiten op de producten van Massive Media. Door deze te ontbinden in een trend- en seizoensbeweging met behulp van lineaire regressie, wordt de restcomponent bepaald waarvan we veronderstellen dat die normaal verdeeld is. Uitschieters kunnen vervolgens met behulp van robuste statistische methoden, zoals de Mediaan Absolute Deviatie (MAD), ontdekt en gerapporteerd worden.De nieuwe implementatie vindt uitschieters in de data die onmogelijk te vinden zijn met het blote oog en geeft ongeveer 40% minder uitschieters dan de bestaande methode.

Algoritme - algorithm. --- CSS - CSS. --- Dashboard. --- HTML5. --- JavaScript - JavaScript. --- Lineaire regressie. --- MAD. --- Media queries. --- Optimalisatie - optimization. --- Python. --- SVG. --- Webapplicatie - web application.

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311 --- Statistiek --- statistiek --- Mathematical statistics --- Statistiek. --- kans --- kanstheorie --- spreiding --- verdeling --- binomiaal --- normaal --- poisson --- steekproeven --- toetsen --- kwadraten --- regressie --- correlatie --- index --- Statistical science

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Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. 'Nonparametric Econometrics' fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data--nominal and ordinal--in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types--continuous, nominal, and ordinal--within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. 'Nonparametric Econometrics' covers all the material necessary to understand and apply nonparametric methods for real-world problems.

Econometrics --- Nonparametric statistics --- 330.0151954 --- econometrie --- regressie-analyse --- wiskundige statistiek --- 303.5 --- AA / International- internationaal --- 330.115 --- Distribution-free statistics --- Statistics, Distribution-free --- Statistics, Nonparametric --- Mathematical statistics --- Economics, Mathematical --- Statistics --- 330.115 Econometrie --- Econometrie --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek) --- Econometrics. --- Nonparametric statistics.