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...
Sábháilte in:
| Formáid: | Online |
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| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Ábhair: | |
| Rochtain ar líne: | ONIX_20240514_9783725807475_513 |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| _version_ | 1869520588494602240 |
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| collection | Directory of Open Access Books |
| description | 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 transform data into information, information into knowledge, and knowledge into actionable decisions. However, the intricacies of big data pose numerous challenges, such as dealing with missing data, high- and ultra-high-dimensional data, response dependencies, time series analysis, and distributed storage. Existing theories, methods, and algorithms for analyzing big data encounter significant hurdles, especially concerning fundamental statistical concepts like estimation, hypothesis testing, confidence intervals, and variable selection, spanning frequentist and Bayesian approaches. This reprint offers an array of tools within the realm of data science aimed at tackling these challenges. It encompasses various topics, including handling measurement errors or missing data, cognitive diagnosis modeling, constructing credit risk scorecards using logistic regression models, geographically weighted regression modeling, privacy protection practices in data mining, clustering methods, and model selection for high-dimensional datasets. Furthermore, it delves into predicting sensitive features under indirect questioning. These discussions aim to provide valuable tools and examples for the practical application of data science. |
| format | Online |
| id | doab-20.500.12854ir-137898 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1378982024-05-14T14:53:48Z Statistical Methods in Data Science and Applications Tang, Niansheng Lee, Shen-Ming 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 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 transform data into information, information into knowledge, and knowledge into actionable decisions. However, the intricacies of big data pose numerous challenges, such as dealing with missing data, high- and ultra-high-dimensional data, response dependencies, time series analysis, and distributed storage. Existing theories, methods, and algorithms for analyzing big data encounter significant hurdles, especially concerning fundamental statistical concepts like estimation, hypothesis testing, confidence intervals, and variable selection, spanning frequentist and Bayesian approaches. This reprint offers an array of tools within the realm of data science aimed at tackling these challenges. It encompasses various topics, including handling measurement errors or missing data, cognitive diagnosis modeling, constructing credit risk scorecards using logistic regression models, geographically weighted regression modeling, privacy protection practices in data mining, clustering methods, and model selection for high-dimensional datasets. Furthermore, it delves into predicting sensitive features under indirect questioning. These discussions aim to provide valuable tools and examples for the practical application of data science. 2024-05-14T14:53:44Z 2024-05-14T14:53:44Z 2024 book ONIX_20240514_9783725807475_513 9783725807475 9783725807482 https://directory.doabooks.org/handle/20.500.12854/137898 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9160 https://mdpi.com/books/pdfview/book/9160 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0748-2 10.3390/books978-3-7258-0748-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725807475 9783725807482 302 open access |
| spellingShingle | 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 Statistical Methods in Data Science and Applications |
| title | Statistical Methods in Data Science and Applications |
| title_full | Statistical Methods in Data Science and Applications |
| title_fullStr | Statistical Methods in Data Science and Applications |
| title_full_unstemmed | Statistical Methods in Data Science and Applications |
| title_short | Statistical Methods in Data Science and Applications |
| title_sort | statistical methods in data science and applications |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20240514_9783725807475_513 |