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Full-Text Articles in Physical Sciences and Mathematics
Functional Mixed Data Clustering With Fourier Basis Smoothing, Ishmael Amartey
Functional Mixed Data Clustering With Fourier Basis Smoothing, Ishmael Amartey
Electronic Theses and Dissertations
Clustering is an important analytical technique that has proven to affect human life positively through its application in cancer research, market segmentation, city planning etc. In this time of growing technological systems, mixed data has seen another face of longitudinal, directional and functional attributes which is worth paying attention to and analyzing. Previous research works on clustering relied largely on the inverse weight technique and B-spline in smoothing data and assessing the performance of various clustering algorithms. In 1971, Gower proposed a method of clustering for mixed variable types which has been extended to include functional and directional variables by …
Performance Comparison Of Imputation Methods For Mixed Data Missing At Random With Small And Large Sample Data Set With Different Variability, Kyei Afari
Electronic Theses and Dissertations
One of the concerns in the field of statistics is the presence of missing data, which leads to bias in parameter estimation and inaccurate results. However, the multiple imputation procedure is a remedy for handling missing data. This study looked at the best multiple imputation methods used to handle mixed variable datasets with different sample sizes and variability along with different levels of missingness. The study employed the predictive mean matching, classification and regression trees, and the random forest imputation methods. For each dataset, the multiple regression parameter estimates for the complete datasets were compared to the multiple regression parameter …
Comparison Of Imputation Methods For Mixed Data Missing At Random, Kaitlyn Heidt
Comparison Of Imputation Methods For Mixed Data Missing At Random, Kaitlyn Heidt
Electronic Theses and Dissertations
A statistician's job is to produce statistical models. When these models are precise and unbiased, we can relate them to new data appropriately. However, when data sets have missing values, assumptions to statistical methods are violated and produce biased results. The statistician's objective is to implement methods that produce unbiased and accurate results. Research in missing data is becoming popular as modern methods that produce unbiased and accurate results are emerging, such as MICE in R, a statistical software. Using real data, we compare four common imputation methods, in the MICE package in R, at different levels of missingness. The …
Clustering Mixed Data: An Extension Of The Gower Coefficient With Weighted L2 Distance, Augustine Oppong
Clustering Mixed Data: An Extension Of The Gower Coefficient With Weighted L2 Distance, Augustine Oppong
Electronic Theses and Dissertations
Sorting out data into partitions is increasing becoming complex as the constituents of data is growing outward everyday. Mixed data comprises continuous, categorical, directional functional and other types of variables. Clustering mixed data is based on special dissimilarities of the variables. Some data types may influence the clustering solution. Assigning appropriate weight to the functional data may improve the performance of the clustering algorithm. In this paper we use the extension of the Gower coefficient with judciously chosen weight for the L2 to cluster mixed data.The benefits of weighting are demonstrated both in in applications to the Buoy data set …