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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Bootstrap-based Statistical Analysis for Multi-period Data-driven Inventory Models with Seasonal Demand
自举 统计分析 季节性需求 多周期数据 驱动库存模型
2023/4/24
We study multi-period inventory control systems in which managers face seasonal demands with unknown distributions and make inventory decisions based on past demand data. It can be shown that a data-d...
Multivariate Regression Shrinkage and Selection by Canonical Correlation Analysis
Adaptive Lasso Canonical Correlation Analysis Multivariate Regression Selection Consistency
2016/1/20
The problem of regression shrinkage and selection for multivariate regression is considered. The goal is to consistently identify those variables relevant for regression. This is done not only for pre...
Can Recent Innovations in Harmonic Analysis ‘Explain’ Key Findings in Natural Image Statistics?
Recent Innovations Natural Image Statistics
2015/8/21
Recently, applied mathematicians have been pursuing the goal of sparse coding of
certain mathematical models of images with edges. They have found by mathematical
analysis that, instead of wavelets ...
Functional Linear Discriminant Analysis for Irregularly Sampled Curves
Classification Filtering Functional data Linear discriminant analysis Low dimensional representation Reduced rank Regularized discriminant analysis Sparse curves
2015/8/21
We introduce a technique for extending the classical method of Linear Discriminant Analysis to data sets where the predictor variables are curves or functions. This procedure, which we call functional...
Constrained ordination analysis with flexible response functions
Canonical correspondence analysis Density estimation Discriminant analysis Likelihood ratio Multinomial and Poisson likelihood Random weights Vegetation succession study
2015/8/21
Canonical correspondence analysis (CCA) is perhaps the most popular multivariate technique used by environmental ecologists for constrained ordination; it is an approximation to the maximum likelihood...
Sparse Principal Component Analysis
Arrays Gene expression Lasso/elastic net Multivariate analysis Singular value decomposition Thresholding
2015/8/21
Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However,PCA suffers from the fact that each principal component is a linear combination of all the or...
Feature Extraction for Nonparametric Discriminant Analysis
ClassiŽ cation Density estimation Dimension reduction LDA Projection pursuit Reduced-rankmodel SAVE
2015/8/21
In high-dimensional classification problems, one is often interested in ?? finding a few important discriminant directions in order to reduce the dimensionality.Fisher’s linear discriminant analysis(L...
Regularized linear discriminant analysis and its application in microarrays
Regularized linear discriminant analysis microarrays
2015/8/21
In this paper, we introduce a modified version of linear discriminant analysis, called the “shrunken centroids regularized discriminant analysis” (SCRDA). This method generalizes the idea of the “near...
We consider the problem of performing interpretable classification in the high-dimensional setting, in which the number of features is very large and the number of observations is limited. This settin...
Genomewide Association Analysis by Lasso Penalized Logistic Regression
Genomewide Association Analysis Lasso Penalized Logistic Regression
2015/8/21
In ordinary regression, imposition of a lasso penalty makes continuous model selection straightforward. Lasso penalized regression is particularly advantageous when the number of predictors far exceed...
Principal components analysis (PCA) is a classical method for the reduction of dimensionality of
data in the form of n observations (or cases) of a vector with p variables. Contemporary data sets
of...
Wavelets have motivated development of a host of new ideas in nonparametric
regression smoothing. Here we apply the tool of exact risk analysis, to understand the
small sample behavior of wavelet es...
Sequential Monte Carlo Methods for Statistical Analysis of Tables
Conditional inference Contingency table Counting problem Exact test Sequential importance sampling Zero–one table
2015/7/8
Sequential Monte Carlo Methods for Statistical Analysis of Tables.
Markov bases for noncommutative Fourier analysis of ranked data
Calibration markov chain monte carlo techniques data sets and statistical analysis
2015/7/8
To calibrate Fourier analysis of S5 ranking data by Markov chain Monte Carlo techniques, a set of moves (Markov basis) is needed. We calculate this basis, and use it to provide a new statistical analy...
The Orthogonal Saturated Parameter Model and its Statistical Analysis
Orthogonal Saturated Parameter Model Statistical Analysis
2015/3/20
The Orthogonal Saturated Parameter Model and its Statistical Analysis.