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Mathematical statistics --- Multivariate analysis --- #ABIB:astp --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Matrices --- Multivariate analysis.

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Mathematical statistics --- Multivariate analysis --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Matrices --- Multivariate analysis.

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Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field.

Multivariate analysis. --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- Management & management techniques

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Nowadays, due to the advancement and significantly rapid growth in the technology, the collection of high-dimensional data is no longer a tedious task. Regardless of considerable advances in technology over the last few decades, the analysis of high-dimensional data faces new challenges concerning interpretation and integration. One of the major problems in high-dimensional data is the occurrence of missing values. The problem is in particular hard to handle when the distributional forms of the variables are different or the variables are measured on different measurement scales (e.g. binary, multi-categorical, continuous, etc.). Whatever the reason, missing data may occur in all areas of applied research. The inadequate handling of missing values may lead to biased results and incorrect inference. The standard statistical techniques for analyzing the data require complete cases without any missing observations. The deletion of the cases with missing information to obtain complete data will not only cause the loss of important information but can also affect inferences. In this dissertation, different imputation techniques using nearest neighbors are developed to address the missing data issues in high-dimensional as well as low dimensional data structures.

Multivariate analysis --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- E-books --- German literature.

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Mathematical statistics --- 519.237 --- Multivariate analysis --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Matrices --- Multivariate statistical methods --- Multivariate analysis. --- 519.237 Multivariate statistical methods