Influence Diagnostics for a Skew Extension of the Grubbs Measurement Error Model

Influence diagnostics methods are extended in this article to the Grubbs model when the unknown quantity x (latent variable) follows a skew-normal distribution. Diagnostic measures are derived from the case-deletion approach and the local influence approach under several perturbation schemes. The observed information matrix to the postulated model and Delta matrices to the corresponding perturbed models are derived. Results obtained for one real data set are reported, illustrating the usefulness of the proposed methodology.

Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors

In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model`s goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright (C) 2008 John Wiley & Sons, Ltd.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Evidence for production of single top quarks and first direct measurement of vertical bar V-tb vertical bar

The D0 Collaboration presents first evidence for the production of single top quarks at the Fermilab Tevatron < p(p)over bar > collider. Using a 0.9 fb(-1) dataset, we apply a multivariate analysis to separate signal from background and measure sigma(< p(p)over bar >-> tb+X,tqb+X)=4.9 +/- 1.4 pb. The probability to measure a cross section at this value or higher in the absence of a signal is 0.035%, corresponding to a 3.4 standard deviation significance. We use the cross section measurement to directly determine the Cabibbo-Kobayashi-Maskawa matrix element that describes the Wtb coupling and find 0.68 model.

Measurement of the t-channel single top quark production cross section

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Model-independent measurement of t-channel single top quark production in p(p)over-bar collisions at,root s=1.96 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Model-independent measurement of the W-boson helicity in top-quark decays at D0

We present the first model-independent measurement of the helicity of W bosons produced in top quark decays, based on a 1 fb(-1) sample of candidate t (t) over bar events in the dilepton and lepton plus jets channels collected by the D0 detector at the Fermilab Tevatron p (p) over bar Collider. We reconstruct the angle theta(*) between the momenta of the down-type fermion and the top quark in the W boson rest frame for each top quark decay. A fit of the resulting cos theta(*) distribution finds that the fraction of longitudinal W bosons f(0)=0.425 +/- 0.166(stat)+/- 0.102(syst) and the fraction of right-handed W bosons f(+)=0.119 +/- 0.090(stat)+/- 0.053(syst), which is consistent at the 30% C.L. with the standard model.

Measurement of the t-Channel Single Top Quark Production Cross Section in pp Collisions at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of the weak mixing angle with the Drell-Yan process in proton-proton collisions at the LHC

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Continuous measurement of cerebral oxygen saturation (rSO 2) for assessment of cardiovascular status during hemorrhagic shock in a swine model

Background: Early trauma care is dependent on subjective assessments and sporadic vital sign assessments. We hypothesized that near-infrared spectroscopy-measured cerebral oxygenation (regional oxygen saturation [rSO 2]) would provide a tool to detect cardiovascular compromise during active hemorrhage. We compared rSO 2 with invasively measured mixed venous oxygen saturation (SvO2), mean arterial pressure (MAP), cardiac output, heart rate, and calculated pulse pressure. Methods: Six propofol-anesthetized instrumented swine were subjected to a fixed-rate hemorrhage until cardiovascular collapse. rSO 2 was monitored with noninvasively measured cerebral oximetry; SvO2 was measured with a fiber optic pulmonary arterial catheter. As an assessment of the time responsiveness of each variable, we recorded minutes from start of the hemorrhage for each variable achieving a 5%, 10%, 15%, and 20% change compared with baseline. Results: Mean time to cardiovascular collapse was 35 minutes ± 11 minutes (54 ± 17% total blood volume). Cerebral rSO 2 began a steady decline at an average MAP of 78 mm Hg ± 17 mm Hg, well above the expected autoregulatory threshold of cerebral blood flow. The 5%, 10%, and 15% decreases in rSO 2 during hemorrhage occurred at a similar times to SvO2, but rSO 2 lagged 6 minutes behind the equivalent percentage decreases in MAP. There was a higher correlation between rSO 2 versus MAP (R =0.72) than SvO2 versus MAP (R =0.55). Conclusions: Near-infrared spectroscopy- measured rSO 2 provided reproducible decreases during hemorrhage that were similar in time course to invasively measured cardiac output and SvO2 but delayed 5 to 9 minutes compared with MAP and pulse pressure. rSO 2 may provide an earlier warning of worsening hemorrhagic shock for prompt interventions in patients with trauma when continuous arterial BP measurements are unavailable. © 2012 Lippincott Williams & Wilkins.

Measurement of the single-top-quark t-channel cross section in pp collisions at √s=7 TeV

A measurement of the single-top-quark t-channel production cross section in pp collisions at √s=7 TeV with the CMS detector at the LHC is presented. Two different and complementary approaches have been followed. The first approach exploits the distributions of the pseudorapidity of the recoil jet and reconstructed top-quark mass using background estimates determined from control samples in data. The second approach is based on multivariate analysis techniques that probe the compatibility of the candidate events with the signal. Data have been collected for the muon and electron final states, corresponding to integrated luminosities of 1.17 and 1.56 fb-1, respectively. The single-top-quark production cross section in the t-channel is measured to be 67.2±6.1 pb, in agreement with the approximate next-to-next-to-leading- order standard model prediction. Using the standard model electroweak couplings, the CKM matrix element |V tb| is measured to be 1.020 ± 0.046 (meas.) ± 0.017 (theor.). © 2012 CERN for the benefit of the CMS collaboration.

Characterization of the variable cow's age at last calving as a measurement of longevity by using the Kaplan-Meier estimator and the Cox model

In most studies on beef cattle longevity, only the cows reaching a given number of calvings by a specific age are considered in the analyses. With the aim of evaluating all cows with productive life in herds, taking into consideration the different forms of management on each farm, it was proposed to measure cow longevity from age at last calving (ALC), that is, the most recent calving registered in the files. The objective was to characterize this trait in order to study the longevity of Nellore cattle, using the Kaplan-Meier estimators and the Cox model. The covariables and class effects considered in the models were age at first calving (AFC), year and season of birth of the cow and farm. The variable studied (ALC) was classified as presenting complete information (uncensored = 1) or incomplete information (censored = 0), using the criterion of the difference between the date of each cow's last calving and the date of the latest calving at each farm. If this difference was >36 months, the cow was considered to have failed. If not, this cow was censored, thus indicating that future calving remained possible for this cow. The records of 11 791 animals from 22 farms within the Nellore Breed Genetic Improvement Program ('Nellore Brazil') were used. In the estimation process using the Kaplan-Meier model, the variable of AFC was classified into three age groups. In individual analyses, the log-rank test and the Wilcoxon test in the Kaplan-Meier model showed that all covariables and class effects had significant effects (P < 0.05) on ALC. In the analysis considering all covariables and class effects, using the Wald test in the Cox model, only the season of birth of the cow was not significant for ALC (P > 0.05). This analysis indicated that each month added to AFC diminished the risk of the cow's failure in the herd by 2%. Nonetheless, this does not imply that animals with younger AFC had less profitability. Cows with greater numbers of calvings were more precocious than those with fewer calvings. Copyright © The Animal Consortium 2012.

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.