A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New simulations advise caution in choosing principal components for multivariate functional data.
Regularized MFPCA smooths multivariate functional data for clearer patterns.
This study improves estimation of the first principal component in multivariate functional data.
A new method uses Gram matrix for efficient multivariate functional principal components.
The paper introduces a method for interpretable principal component analysis of high-dimensional time series.
PCA simplifies multivariate extreme data analysis.
A method for representing and comparing categorical trajectories using multivariate functional principal components.
This paper improves PPCA robustness using -distributions.
The dynamic nature of air quality chemistry and transport makes it difficult to identify the mixture of air pollutants for a region. In this study of air quality in the Houston metropolitan area we apply dynamic principal component analysis (DPCA) to a normalized multivariate time series of daily concentration measurem…
We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension to scale with the series length . We treat the transition matrix of time series as a nuisance parameter and directly apply sparse principal component…
Two new models forecast multiple subpopulations' mortality, outperforming existing methods.
The paper tackles noisy functional data by exploring a multivariate perspective.
The paper introduces a new method for detecting financial data outliers.
Principal Components Analysis is a widely used technique for dimension reduction and characterization of variability in multivariate populations. Our interest lies in studying when and why the rotation to principal components can be used effectively within a response-predictor set relationship in the context of mode hu…
New sampling strategy preserves relationships in multivariate scientific data.
A new method for sparse PCA using orthogonal rotations and soft-thresholding.
FCPCA fuzzy clusters high-dimensional time series data efficiently.
We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to non-linear methods, linear dimensionality reduction techniques have the advantag…
Study explores K-means clustering of variables and its relation to PCA.
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA). The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are multivariate Gaussian. COCA improves upon PCA and sparse PCA in three as…
A novel fuzzy clustering method for multivariate time series.
Principal component analysis (PCA) is a statistical technique commonly used in multivariate data analysis. However, PCA can be difficult to interpret and explain since the principal components (PCs) are linear combinations of the original variables. Sparse PCA (SPCA) aims to balance statistical fidelity and interpretab…
We propose a penalized orthogonal-components regression (POCRE) for large p small n data. Orthogonal components are sequentially constructed to maximize, upon standardization, their correlation to the response residuals. A new penalization framework, implemented via empirical Bayes thresholding, is presented to effecti…
In this paper, we provide explicit formulas, in terms of the covariances of sample covariances or sample correlations, for the asymptotic covariances of unrotated factor loading estimates and unique variance estimates. These estimates are extracted from least square, principal, iterative principal component, alpha or i…
Sequential or online dimensional reduction is of interests due to the explosion of streaming data based applications and the requirement of adaptive statistical modeling, in many emerging fields, such as the modeling of energy end-use profile. Principal Component Analysis (PCA), is the classical way of dimensional redu…
The first order behavior of multivariate heavy-tailed random vectors above large radial thresholds is ruled by a limit measure in a regular variation framework. For a high dimensional vector, a reasonable assumption is that the support of this measure is concentrated on a lower dimensional subspace, meaning that certai…
Two strategies for embedding new data points from proximity data are explored.
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random variables. CA has found applications in fields ranging from epidemiology to social sciences; however, current methods do not scale to large, hig…
Proposes a new method for multivariate functional regression.
Multivariate binary data is becoming abundant in current biological research. Logistic principal component analysis (PCA) is one of the commonly used tools to explore the relationships inside a multivariate binary data set by exploiting the underlying low rank structure. We re-expressed the logistic PCA model based on …
Study uses VIX for zero-coupon Treasury rates, proving long-term stability and returns.
Sensor data analysis plays a key role in health assessment of critical equipment. Such data are multivariate and exhibit nonlinear relationships. This paper describes how one can exploit nonlinear dimension reduction techniques, such as the t-distributed stochastic neighbor embedding (t-SNE) and kernel principal compon…
Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community. This paper partially redresses this imbalance by extending some standard probabilistic modelling tools to the circular domain. First w…
HPPCA improves imputation of longitudinal data with missing values.
New algorithm balances spatial data approximation and prediction accuracy.
Paper speeds up large foundation models for time series data.
This paper presents the nonparametric inference for nonlinear volatility functionals of general multivariate Itô semimartingales, in high-frequency and noisy setting. Pre-averaging and truncation enable simultaneous handling of noise and jumps. Second-order expansion reveals explicit biases and a pathway to bias correc…
CAVI converges exponentially fast for Bayesian PCA models.
This paper critiques flawed MVTS anomaly detection evaluation methods and proposes a simple baseline.
Generative neural networks model multivariate time series data.
RFPCA improves robustness of FPCA for matrix data.
Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. In this paper, we consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multiv…
Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables. Note that the principal components are obtained from only explanatory variables and not considered with the response variable. To addre…
Introduces geometric formulation of EM algorithm for robust inference and various applications.
PCHAL and PCHAR use principal components to speed up HAL and HAR methods.
Efficient private matrix analysis algorithms for recent variants.
Software helps teach latent variable methods in multivariate data analytics.
Conventional principal component analysis (PCA) finds a principal vector that maximizes the sum of second powers of principal components. We consider a generalized PCA that aims at maximizing the sum of an arbitrary convex function of principal components. We present a gradient ascent algorithm to solve the problem. Fo…