missing data estimation modelling de mohamed noor (6 resultados)

Taschenbuch. Condición: Neu. Missing Data Estimation And Modelling Via Probability Distributions | A Case Study Of Air Pollutant Data In Malaysia | Norazian Mohamed Noor (u. a.) | Taschenbuch | 80 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783846586013 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides simple applications of single imputation methods in replacing missing values and latermodel the dataset using common probability distributions i.e. Weibull, gamma, lognormal etc. The first chapter reviews the theory on missing data mechanism, single and multiple imputations and the types of software available to fill the missing data. Then, the assessment of few single imputation methods were described in chapter 2. The selections of the most appropriate method for the observations were also revealed. In the last chapter, the readers will be exposed on how to model the air pollutant data using probability distributions. The most fitted distributions were also selected after calculating the performance indicators and finally, the most fitted distribution will be used to estimate the return period for next year. Hopefully with this humble publication, the interest among the readers will be developed to explore this new chapter of research thus improving the quality of dataset for better analysis. Furthermore, the good quality data can be model for the future. 80 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mohamed Noor NorazianNorazian Mohamed Noor is currently a PhD student at Universiti Sains Malaysia (USM). She is actively doing research in air pollution modeling specializing in imputation techniques. Her achievements include 35 pub.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides simple applications of single imputation methods in replacing missing values and latermodel the dataset using common probability distributions i.e. Weibull, gamma, lognormal etc. The first chapter reviews the theory on missing data mechanism, single and multiple imputations and the types of software available to fill the missing data. Then, the assessment of few single imputation methods were described in chapter 2. The selections of the most appropriate method for the observations were also revealed. In the last chapter, the readers will be exposed on how to model the air pollutant data using probability distributions. The most fitted distributions were also selected after calculating the performance indicators and finally, the most fitted distribution will be used to estimate the return period for next year. Hopefully with this humble publication, the interest among the readers will be developed to explore this new chapter of research thus improving the quality of dataset for better analysis. Furthermore, the good quality data can be model for the future.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides simple applications of single imputation methods in replacing missing values and latermodel the dataset using common probability distributions i.e. Weibull, gamma, lognormal etc. The first chapter reviews the theory on missing data mechanism, single and multiple imputations and the types of software available to fill the missing data. Then, the assessment of few single imputation methods were described in chapter 2. The selections of the most appropriate method for the observations were also revealed. In the last chapter, the readers will be exposed on how to model the air pollutant data using probability distributions. The most fitted distributions were also selected after calculating the performance indicators and finally, the most fitted distribution will be used to estimate the return period for next year. Hopefully with this humble publication, the interest among the readers will be developed to explore this new chapter of research thus improving the quality of dataset for better analysis. Furthermore, the good quality data can be model for the future.