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Contributions to statistical learning and statistical quantification in

Contributions to statistical learning and statistical quantification in 2026

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The thesis by Xinwei Deng presents significant contributions to statistical learning and quantification methods in the context of nanomaterials. It is divided into two main parts: the first focuses on various statistical learning methodologies, including Gaussian covariance matrix estimation, robust kernel principal component analysis, active learning for money laundering detection, and factor logit models for multi-category classification. The second part addresses the statistical quantification of elastic deformation in nanomaterials, proposing a new method called Sequential Profile Adjustment by Regression (SPAR) to improve the accuracy of elastic modulus estimates by accounting for systematic biases and measurement errors. The research emphasizes the importance of integrating statistical modeling with physical principles to enhance data analysis in nanotechnology.

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What is the statistical learning?

Statistical learning is a rapid and robust mechanism that enables adults and infants to extract patterns of stimulation embedded in both language and visual domains. Importantly, statistical learning operates implicitly, without instruction, through mere exposure to a set of input stimuli.

What is the difference between statistics and statistical learning?

Learning falls into many categories, including supervised learning, unsupervised learning, online learning, and reinforcement learning. From the perspective of statistical learning theory, supervised learning is best understood. Supervised learning involves learning from a training set of data.

What is statistical and not statistical?

Statistics is a mathematical science that studies the collection, analysis, interpretation, and presentation of data. Statistical/Machine Learning is the application of statistical methods (mostly regression) to make predictions about unseen data.

What is the difference between machine learning and statistical learning?

Machine learning (ML) and statistics are important in data analysis but serve different purposes. Machine learning focuses on how computers use data to learn, and statistics help interpret data to solve problems. Ultimately, ML and statistics complement each other in problem-solving and making predictions.

What is statistical learning and what statistical learning is not?

Statistical/Machine Learning is the application of statistical methods (mostly regression) to make predictions about unseen data. Statistical Learning and Machine Learning are broadly the same thing. The main distinction between them is in the culture.