Search results for "Statistics::Methodology"
showing 10 items of 71 documents
"Table 8" 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 II.
"Table 9" 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 II.
"Table 2" of "Measurement of the $\nu_\mu$ CCQE cross section on carbon with the ND280 detector at T2K"
2016
The fractional covariance matrix corresponding to the errors shown in Figure 7.
"Table 115" of "Measurements of $t\bar{t}$ differential cross-sections of highly boosted top quarks decaying to all-hadronic final states in $pp$ col…
2019
$|y^{t}|$ covariance matrix for absolute differential cross-section in parton level
Shrinkage and spectral filtering of correlation matrices: A comparison via the Kullback-Leibler distance
2007
The problem of filtering information from large correlation matrices is of great importance in many applications. We have recently proposed the use of the Kullback-Leibler distance to measure the performance of filtering algorithms in recovering the underlying correlation matrix when the variables are described by a multivariate Gaussian distribution. Here we use the Kullback-Leibler distance to investigate the performance of filtering methods based on Random Matrix Theory and on the shrinkage technique. We also present some results on the application of the Kullback-Leibler distance to multivariate data which are non Gaussian distributed.
Nonlocal symmetry for QED
1993
We demonstrate that QED exhibits a previously unobserved noncovariant, nonlocal symmetry. Some consequences are discussed.
Appendix C. Posterior distributions of the CR-SEM parameters (conditional on the covariates being in the model).
2016
Posterior distributions of the CR-SEM parameters (conditional on the covariates being in the model).
Including Covariates in the ETAS Model Triggered Seismicity
2020
The paper proposes a stochastic process that improves the assessment of seismic events in space and time, considering a contagion model (branching process) within a regression-like framework to take covariates into account. The proposed approach develops the Forward Likelihood for prediction (FLP) method for estimating the ETAS model, including covariates in the model specification of the epidemic component. A simulation study is carried out for analysing the misspecification model effect under several scenarios. Also an application to the Italian catalogue is reported, together with the reference to the developed R package.
Regression with Imputed Covariates: A Generalized Missing Indicator Approach
2011
A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations with the imputed values to gain precision may lead to bias. In this paper we formalize this trade-off by showing that one can augment the regression model with a set of auxiliary variables so as to obtain, under weak assumptions about the imputations, the same unbiased estimator of the parameters of interest as complete-case analysis. Given this augmented model, the bias-precision trade-off may then…
A Generalized Missing-Indicator Approach to Regression with Imputed Covariates
2011
We consider estimation of a linear regression model using data where some covariate values are missing but imputations are available to fill in the missing values. This situation generates a tradeoff between bias and precision when estimating the regression parameters of interest. Using only the subsample of complete observations does not cause bias but may imply a substantial loss of precision because the complete cases may be too few. On the other hand, filling in the missing values with imputations may cause bias. We provide the new Stata command gmi, which handles such tradeoff by using either model reduction or Bayesian model averaging techniques in the context of the generalized miss…