Results 41 to 50 of about 719 (114)

Covariance regularization by thresholding

, 2008
This paper considers regularizing a covariance matrix of $p$ variables estimated from $n$ observations, by hard thresholding. We show that the thresholded estimate is consistent in the operator norm as long as the true covariance matrix is sparse in a ...
Bickel, Peter J., Levina, Elizaveta
core   +4 more sources

Functional approach for excess mass estimation in the density model

, 2007
We consider a multivariate density model where we estimate the excess mass of the unknown probability density $f$ at a given level $\nu>0$ from $n$ i.i.d. observed random variables.
Butucea, Cristina, Mougeot, Mathilde, Tribouley, Karine   +2 more
core   +1 more source

Integration GSTARIMA with deep neural network to enhance prediction accuracy on rainfall data

Systems Science & Control Engineering
This study aimed to improve rainfall prediction accuracy by integrating spatio-temporal Generalized Autoregressive Integrated Moving Average (GSTARIMA) with Deep Neural Network (DNN) techniques.
Devi Munandar, Budi Nurani Ruchjana, Atje Setiawan Abdullah, Hilman Ferdinandus Pardede   +3 more
doaj   +1 more source

The Masked Sample Covariance Estimator: An Analysis via Matrix Concentration Inequalities [PDF]

, 2011
Covariance estimation becomes challenging in the regime where the number p of variables outstrips the number n of samples available to construct the estimate. One way to circumvent this problem is to assume that the covariance matrix is nearly sparse and
Chen, Richard Y., Gittens, Alex, Tropp, Joel A.   +2 more
core   +1 more source

A Sensitivity Matrix Methodology for Inverse Problem Formulation [PDF]

, 2009
We propose an algorithm to select parameter subset combinations that can be estimated using an ordinary least-squares (OLS) inverse problem formulation with a given data set.
Banks, H. T., Capaldi, Alex, Cintron-Arias, Ariel, Lloyd, Alun L.   +3 more
core   +2 more sources

Testing of a Structures Covariance Matrix for Three-Level Repeated Measures Data. [PDF]

This paper considers the problem of estimating, and testing for, a Kronecker product covariance structure of three-level (multiple time points (p), multiple sites (u), and multiple response variables (q)) multivariate data.
Anuradha Roy, Ricardo Leiva
core  

Using sums-of-squares to prove Gaussian product inequalities

Dependence Modeling
The long-standing Gaussian product inequality (GPI) conjecture states that E[∏j=1n∣Xj∣yj]≥∏j=1nE[∣Xj∣yj]E\left[{\prod }_{j=1}^{n}{| {X}_{j}| }^{{y}_{j}}]\ge {\prod }_{j=1}^{n}E\left[{| {X}_{j}| }^{{y}_{j}}] for any centered Gaussian random vector (X1 ...
Russell Oliver, Sun Wei
doaj   +1 more source

Assessing copula models for mixed continuous-ordinal variables

Dependence Modeling
Vine pair-copula constructions exist for a mix of continuous and ordinal variables. In some steps, this can involve estimating a bivariate copula for a pair of mixed continuous-ordinal variables.
Pan Shenyi, Joe Harry
doaj   +1 more source

Ultrasensitive Detection of Circulating LINE-1 ORF1p as a Specific Multicancer Biomarker. [PDF]

Cancer Discov, 2023
Taylor MS, Wu C, Fridy PC, Zhang SJ, Senussi Y, Wolters JC, Cajuso T, Cheng WC, Heaps JD, Miller BD, Mori K, Cohen L, Jiang H, Molloy KR, Chait BT, Goggins MG, Bhan I, Franses JW, Yang X, Taplin ME, Wang X, Christiani DC, Johnson BE, Meyerson M, Uppaluri R, Egloff AM, Denault EN, Spring LM, Wang TL, Shih IM, Fairman JE, Jung E, Arora KS, Yilmaz OH, Cohen S, Sharova T, Chi G, Norden BL, Song Y, Nieman LT, Pappas L, Parikh AR, Strickland MR, Corcoran RB, Mustelin T, Eng G, Yilmaz ÖH, Matulonis UA, Chan AT, Skates SJ, Rueda BR, Drapkin R, Klempner SJ, Deshpande V, Ting DT, Rout MP, LaCava J, Walt DR, Burns KH.   +58 more
europepmc   +1 more source

A note on the choice of the number of slices in sliced inverse regression [PDF]

Sliced inverse regression (SIR) is a clever technique for reducing the dimension of the predictor in regression problems, thus avoiding the curse of dimensionality. There exist many contributions on various aspects of the performance of SIR.
Becker, Claudia, Gather, Ursula
core