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Technometrics, 2000
Preface Notation 1. Fundamental Concepts 2. Examples of Probability Functions 3. The Monte Carlo Method 4. Statistical Tests 5. General Concepts of Parameter Estimation 6. The Method of Maximum Likelihood 7. The Method of Least Squares 8. The Method of Moments 9. Statistical Errors, Confidence Intervals and Limits 10.
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Preface Notation 1. Fundamental Concepts 2. Examples of Probability Functions 3. The Monte Carlo Method 4. Statistical Tests 5. General Concepts of Parameter Estimation 6. The Method of Maximum Likelihood 7. The Method of Least Squares 8. The Method of Moments 9. Statistical Errors, Confidence Intervals and Limits 10.
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WIREs Cognitive Science, 2010
AbstractBayesian methods have garnered huge interest in cognitive science as an approach to models of cognition and perception. On the other hand, Bayesian methods for data analysis have not yet made much headway in cognitive science against the institutionalized inertia of 20th century null hypothesis significance testing (NHST).
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AbstractBayesian methods have garnered huge interest in cognitive science as an approach to models of cognition and perception. On the other hand, Bayesian methods for data analysis have not yet made much headway in cognitive science against the institutionalized inertia of 20th century null hypothesis significance testing (NHST).
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Prehospital and Disaster Medicine
AbstractA common and unfortunate error in statistical analysis is the failure to account for dependencies in the data. In many studies, there is a set of individual participants or experimental objects where two observations are made on each individual or object. This leads to a natural pairing of data.
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AbstractA common and unfortunate error in statistical analysis is the failure to account for dependencies in the data. In many studies, there is a set of individual participants or experimental objects where two observations are made on each individual or object. This leads to a natural pairing of data.
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Chaos: An Interdisciplinary Journal of Nonlinear Science
The acceleration in the field of data science is well known [see, e.g., D. Donoho, J. Comput. Graph. Stat. 26(4), 745–766 (2017) and references therein]. Improvements in technology for acquisition, storage, and processing have made unheard of amounts of data available to scientists; in parallel with that, the pace of methodological advance has also ...
Elizabeth Bradley +2 more
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The acceleration in the field of data science is well known [see, e.g., D. Donoho, J. Comput. Graph. Stat. 26(4), 745–766 (2017) and references therein]. Improvements in technology for acquisition, storage, and processing have made unheard of amounts of data available to scientists; in parallel with that, the pace of methodological advance has also ...
Elizabeth Bradley +2 more
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2005
Intelligent Data Analysis (IDA) is an interdisciplinary study concerned with the effective analysis of data. IDA draws the techniques from diverse fields, including artificial intelligence, databases, high-performance computing, pattern recognition, and statistics.
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Intelligent Data Analysis (IDA) is an interdisciplinary study concerned with the effective analysis of data. IDA draws the techniques from diverse fields, including artificial intelligence, databases, high-performance computing, pattern recognition, and statistics.
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Analysis of Phosphoproteomics Data
2010Regulation of protein phosphorylation plays an important role in many cellular processes, particularly in signal transduction. Diseases such as cancer and inflammation are often linked to aberrant signaling pathways. Mass spectrometry-based methods allow monitoring the phosphorylation status in an unbiased and quantitative manner.
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