Search results for "Statistics::Methodology"
showing 10 items of 71 documents
"Table 5" of "Measurements of normalized differential cross-sections for ttbar production in pp collisions at sqrt(s) = 7 TeV using the ATLAS detecto…
2016
Bin-wise full covariance matrices for the normalized differential cross-sections of the hadronically decaying top-quark PT. The elements of the covariance matrices are in units of TeV$^{-2}$.
"Table 6" of "Measurement of the differential cross-section of highly boosted top quarks as a function of their transverse momentum in $\sqrt{s}$ = 8…
2016
Covariance matrix for the parton-level differential cross-section. The elements of the covariance matrix are in units of ab^2/GeV^2.
"Table 5" of "Measurement of the differential cross-section of highly boosted top quarks as a function of their transverse momentum in $\sqrt{s}$ = 8…
2016
Covariance matrix for the particle-level differential cross-section. The elements of the covariance matrix are in units of ab^2/GeV^2.
Threshold cointegration and nonlinear adjustment between goods and services inflation in the United States
2006
In this paper, we model the long-run relationship between goods and services inflation for the United States over the period 1968:1–2003:3. Our empirical methodology makes use of recent developments on threshold cointegration that consider the possibility of a nonlinear relationship between the two inflation series. According to our results, the null hypothesis of linear cointegration would be rejected in favor of a two-regime threshold cointegration model. Consequently, we could expect a cointegrating relationship only when the divergence between services inflation and goods inflation is above the threshold point estimate.
Stationary subspace analysis based on second-order statistics
2021
In stationary subspace analysis (SSA) one assumes that the observable p-variate time series is a linear mixture of a k-variate nonstationary time series and a (p-k)-variate stationary time series. The aim is then to estimate the unmixing matrix which transforms the observed multivariate time series onto stationary and nonstationary components. In the classical approach multivariate data are projected onto stationary and nonstationary subspaces by minimizing a Kullback-Leibler divergence between Gaussian distributions, and the method only detects nonstationarities in the first two moments. In this paper we consider SSA in a more general multivariate time series setting and propose SSA method…
Single-trial Connectivity Estimation through the Least Absolute Shrinkage and Selection Operator.
2019
Methods based on the use of multivariate autoregressive models (MVAR) have proved to be an accurate tool for the estimation of functional links between the activity originated in different brain regions. A well-established method for the parameters estimation is the Ordinary Least Square (OLS) approach, followed by an assessment procedure that can be performed by means of Asymptotic Statistic (AS). However, the performances of both procedures are strongly influenced by the number of data samples available, thus limiting the conditions in which brain connectivity can be estimated. The aim of this paper is to introduce and test a regression method based on Least Absolute Shrinkage and Selecti…
Information Dynamics Analysis: A new approach based on Sparse Identification of Linear Parametric Models*
2020
The framework of information dynamics allows to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of a complex network. The information transfer from one process to another can be quantified through Transfer Entropy, and under the assumption of joint Gaussian variables it is strictly related to the concept of Granger Causality (GC). According to the most recent developments in the field, the computation of GC entails representing the processes through a Vector Autoregressive (VAR) model and a state space (SS) model typically identified by means of the Ordinary Least Squares (OLS). In this work, we propose a new identification …
"Table 5" of "Measurement of double-differential muon neutrino charged-current interactions on C$_8$H$_8$ without pions in the final state using the …
2017
Covariance matrix for flux normalization error (fully correlated) in Analysis I.
"Table 3" of "Measurement of double-differential muon neutrino charged-current interactions on C$_8$H$_8$ without pions in the final state using the …
2017
Covariance matrix for shape systematics error in Analysis I.
"Table 4" of "Measurement of double-differential muon neutrino charged-current interactions on C$_8$H$_8$ without pions in the final state using the …
2017
Covariance matrix for statistical errors in Analysis I.