计量经济分析 = Econometric analysis

Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.

https://repository.library.noaa.gov/view/noaa/28862/noaa_28862_DS1.pdf

Best practices for analysing microbiomes.

Statistical Foundations of Data ScienceNew Perspectives and Challenges in Econophysics and SociophysicsStatistical Learning with SparsityMathematical Foundations of Infinite-Dimensional Statistical ModelsStatistics and Analysis of ShapesMultivariate StatisticsHigh-Dimensional Covariance EstimationPrinciples and Methods for Data ScienceHigh-Dimensional StatisticsHigh-Dimensional ProbabilityHandbook of Financial Econometrics and StatisticsStatistics for High-Dimensional DataRegularization in High-dimensional StatisticsAnalysis of Multivariate and High-Dimensional DataProbability and ComputingLarge Sample Covariance Matrices and High-Dimensional Data AnalysisHigh-Dimensional Data Analysis in Cancer ResearchHandbook of Big Data AnalyticsIntroduction to Clustering Large and High-Dimensional DataAnalyzing High-Dimensional Gene Expression and DNA Methylation Data with RSufficient Dimension ReductionHandbook of Big DataHandbook of Mixture AnalysisHigh Dimensional Probability VIStatistical Analysis for High-Dimensional DataHigh-Dimensional ProbabilitySpectral Analysis of Large Dimensional Random MatricesBig Data AnalyticsIntroduction to High-Dimensional StatisticsGeometric Structure of High-Dimensional Data and Dimensionality ReductionApplied Biclustering Methods for Big and High-Dimensional Data Using RBig and Complex Data AnalysisHigh-dimensional Data AnalysisFunctional Statistics and Related FieldsHandbook of Data VisualizationNonlinear Dimensionality ReductionModern Statistics for Modern BiologySparse Modeling for Image and Vision ProcessingModeling and Stochastic Learning for Forecasting in High DimensionsContributions to Fault Detection and Diagnosis with High-dimensional Data

Statistics for High-Dimensional Data: Methods, Theory and Applications

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. 
We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Forecasting: theory and practice

Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common problem in applied statistics is the presence of outliers in the data. This paper combines robust regression and sparse model estimation. A robust and sparse estimator is introduced by adding an L1penalty on the coefficient estimates to the well-known least trimmed squares (LTS) estimator. The breakdown point of this sparse LTS estimator is derived, and a fast algorithm for its computation is proposed. In addition, the sparse LTS is applied to protein and gene expression data of the NCI-60 cancer cell panel. Both a simulation study and the real data application show that the sparse LTS has better prediction performance than its competitors in the presence of leverage points.

Sparse least trimmed squares regression for analyzing high-dimensional large data sets

Volatility Spillovers in Commodity Markets: A Large t-Vector AutoRegressive Approach

Canonical correlation analysis (CCA) describes the associations between two sets of variables by maximizing the correlation between linear combinations of the variables in each dataset. However, in high-dimensional settings where the number of variables exceeds the sample size or when the variables are highly correlated, traditional CCA is no longer appropriate. This paper proposes a method for sparse CCA. Sparse estimation produces linear combinations of only a subset of variables from each dataset, thereby increasing the interpretability of the canonical variates. We consider the CCA problem from a predictive point of view and recast it into a regression framework. By combining an alternating regression approach together with a lasso penalty, we induce sparsity in the canonical vectors. We compare the performance with other sparse CCA techniques in different simulation settings and illustrate its usefulness on a genomic dataset.

/pdf/sparse-canonical-correlation-analysis-from-a-predictive-jfggg5r6ue.pdf

Sparse canonical correlation analysis from a predictive point of view.

Identifying demand effects in a large network of product categories

Canonical correlation analysis (CCA) describes the associations between two sets of variables by maximizing the correlation between linear combinations of the variables in each data set. However, in high-dimensional settings where the number of variables exceeds the sample size or when the variables are highly correlated, traditional CCA is no longer appropriate. This paper proposes a method for sparse CCA. Sparse estimation produces linear combinations of only a subset of variables from each data set, thereby increasing the interpretability of the canonical variates. We consider the CCA problem from a predictive point of view and recast it into a regression framework. By combining an alternating regression approach together with a lasso penalty, we induce sparsity in the canonical vectors. We compare the performance with other sparse CCA techniques in different simulation settings and illustrate its usefulness on a genomic data set.

Sparse canonical correlation analysis from a predictive point of view

Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series However, as the number of component series is increased, the VAR model becomes overparameterized Several authors have addressed this issue by incorporating regularized approaches, such as the lasso in VAR estimation Traditional approaches address overparameterization by selecting a low lag order, based on the assumption of short range dependence, assuming that a universal lag order applies to all components Such an approach constrains the relationship between the components and impedes forecast performance The lasso-based approaches work much better in high-dimensional situations but do not incorporate the notion of lag order selection 
We propose a new class of hierarchical lag structures (HLag) that embed the notion of lag selection into a convex regularizer The key modeling tool is a group lasso with nested groups which guarantees that the sparsity pattern of lag coefficients honors the VAR's ordered structure The HLag framework offers three structures, which allow for varying levels of flexibility A simulation study demonstrates improved performance in forecasting and lag order selection over previous approaches, and a macroeconomic application further highlights forecasting improvements as well as HLag's convenient, interpretable output

Ines Wilms

Papers

Volatility Spillovers in Commodity Markets: A Large t-Vector AutoRegressive Approach

Sparse canonical correlation analysis from a predictive point of view.

Identifying demand effects in a large network of product categories

Sparse canonical correlation analysis from a predictive point of view

High Dimensional Forecasting via Interpretable Vector Autoregression