Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 14 of 14 for “"Missing Value"”.
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The estimation of a missing value in a lattice design using inter- and intra-block information
… introduced by Cornish⁴ for estimating a missing value in a lattice design, has been extended here and modified to include not only intra-block information, but also inter-block information as well. The analysis of the lattice design has been presented with some simplification applicable …
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Missing values in covariance in the case of the randomized block
The formula and theory for estimating a missing value in the case of covariance in a randomized block has been presented in this paper. It has also been found that the formula given corresponds to Yates’ formula for a missing value in a randomized block when there is only one variable present in …
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Probabilistic models for multi-relational data analysis.
… multi-relational clustering. (2) To predict missing entries, i.e., multi-relational missing value prediction. Clustering and missing value prediction give us a better understanding of data and help us with decision making. For example, clusters of users and movies, as well as whether each …
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Analysis of variance of a balanced incomplete block design with missing observations
… in this paper is that of estimating several missing values and analyzing the resulting augmented data in a balanced incomplete block design. The estimates are obtained by Yates' procedure of minimizing the error sum of squares. Explicit formulae are obtained for all cases involving not more …
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Using K-means Clustering and Similarity Measure to Deal with Missing Rating in Collaborative Filtering Recommendation Systems
… comparison method predicts and fills up the missing value in sparsity dataset to enhance the data density which boosts the recommendation quality. This thesis uses MovieLens dataset to investigate the proposed method, which yields amazing experimental outcome on a large sparsity data set that …
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Analysis of Growth Curves Under Some Special Covariance Structures
… of unbalanced data, such as, monotone data, data missing from any occasion, and data observed at unequally spaced time points.</p> <p>Our main results are: (1) derivation of the formulae for the maximum likelihood estimates (MLEs) of the parameters involved, (2) construction of the tests for …
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A Study of Machine Learning Approaches for Biomedical Signal Processing
… studies two important preprocessing topics: missing value imputation and between-sample normalization. Missing data is a major issue in quantitative proteomics data analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, comparative …
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Predicting Suicide Risk Among Youths Using Machine Learning Methods
… USA. Various preprocessing techniques such as missing value imputation, feature selection, and sampling techniques for handling the imbalanced ratio of the class label were applied to the dataset. The data was partitioned into a training dataset (70%) and a test dataset (30%) using a stratified …
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Change-over designs
… expected mean squares, efficiencies and missing value formulas. A list of designs is presented in an appendix.
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On semiparametric regression and data mining
… mining applications including: model selection, missing value analysis, outliers and heteroscedastic noise. We focus on function estimation using penalised splines via mixed model methodology (Wahba 1990; Speed 1991; Ruppert et al. 2003). In dealing with the difficulties associated with data …
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Design and Evaluation of an AI-Driven Pipeline for Synthetic Tabular Data Generation
… robust preprocessing techniques and Missing Value Imputation (MVI) as foundational steps. A formal evaluation framework was developed to assess the quality of synthetic data based on three core dimensions: Fidelity – measuring statistical similarities between synthetic and real data; …
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Improving reproducibility and standards in quantitative N-Glycoproteomic data
… datasets with small sample sizes and high missing value rates. N-Glycans are inherently interrelated by the biosynthetic network that they’re processed in, and as a result they have a lot of shared information and chemical properties that make identification and quantification more …
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Applications of Bayesian networks in natural hazard assessments
… and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the …
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Analysis based on incomplete data
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01