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The document is a doctoral thesis by Xinwei Deng, focusing on contributions to statistical learning and quantification in nanomaterials. It consists of two main parts: the first part discusses various statistical learning methodologies including Gaussian covariance matrix estimation, robust kernel principal component analysis, active learning for money laundering detection, and factor logit models. The second part addresses statistical approaches for quantifying the elastic deformation of nanomaterials, particularly through a new method called Sequential Profile Adjustment by Regression (SPAR), which aims to improve the accuracy of elastic modulus estimates by accounting for systematic biases in experimental data. The thesis emphasizes the importance of integrating statistical modeling with physical principles to enhance measurement reliability in nanotechnology applications.

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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.