Statistical Methods in Data Science and Applications

The rise of big data has significantly elevated the significance of data science, catalyzing extensive research across multiple fields, including mathematics, statistics, computer science, and artificial intelligence. Data science encompasses modeling, computation, and learning processes to transfor...

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Bibliographic Details
Format:	Online
Language:	English
Published:	MDPI - Multidisciplinary Digital Publishing Institute 2024
Subjects:	meta learning data classification hybrid sine and cosine algorithm Wilcoxon signed rank test multiple application scenario datasets model selection nonparametric additive models nonparametric smoothing ridge estimation data masking multiplicative noise data mining sample size calculation clustering correlation REML multivariate linear mixed models GWNR linear estimator mixed estimator spatial data unbiased bootstrap resampling imputation non-inferiority assessment non-ignorable missing data three-arm trial bootstrap expectation-maximization (EM) algorithm latent class likelihood ratio test maximum likelihood randomized response sensitive attribute credit risk scorecards hypothesis testing population stability simulation biomarkers correction for attenuation measurement error Poisson binomial distribution logistic regression data aggregation likelihood numerical optimization indirect questioning non-randomized response technique randomized response technique statistical methods model averaging asymptotic optimality HRCp varying-coefficient partially linear model missing data otsfeatures ordinal time series feature extraction cumulative probabilities R package cognitive diagnosis model DINA model penalized likelihood Shannon entropy EM algorithm surrogate zero-inflated data thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics
Online Access:	ONIX_20240514_9783725807475_513
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Statistical Methods in Data Science and Applications

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