Search for the standard model Higgs boson decaying to a bbˉb\bar{b} pair in events with no charged leptons and large missing transverse energy using the full CDF data set

We report on a search for the standard model Higgs boson produced in association with a vector boson in the full data set of proton-antiproton collisions at $\sqrt{s} = 1.96$ TeV recorded by the CDF II detector at the Tevatron, corresponding to an integrated luminosity of 9.45 fb$^{-1}$. We consider events having no identified charged lepton, a transverse energy imbalance, and two or three jets, of which at least one is consistent with originating from the decay of a $b$ quark. We place 95% credibility level upper limits on the production cross section times standard model branching fraction for several mass hypotheses between 90 and $150 \mathrm{GeV}/c^2$. For a Higgs boson mass of $125 \mathrm{GeV}/c^2$, the observed (expected) limit is 6.7 (3.6) times the standard model prediction.Comment: Accepted by Phys. Rev. Let

Wses Jerusalem Guidelines For Diagnosis And Treatment Of Acute Appendicitis

Acute appendicitis (AA) is among the most common cause of acute abdominal pain. Diagnosis of AA is challenging; a variable combination of clinical signs and symptoms has been used together with laboratory findings in several scoring systems proposed for suggesting the probability of AA and the possible subsequent management pathway. The role of imaging in the diagnosis of AA is still debated, with variable use of US, CT and MRI in different settings worldwide. Up to date, comprehensive clinical guidelines for diagnosis and management of AA have never been issued. In July 2015, during the 3rd World Congress of the WSES, held in Jerusalem (Israel), a panel of experts including an Organizational Committee and Scientific Committee and Scientific Secretariat, participated to a Consensus Conference where eight panelists presented a number of statements developed for each of the eight main questions about diagnosis and management of AA. The statements were then voted, eventually modified and finally approved by the participants to The Consensus Conference and lately by the board of co-authors. The current paper is reporting the definitive Guidelines Statements on each of the following topics: 1) Diagnostic efficiency of clinical scoring systems, 2) Role of Imaging, 3) Non-operative treatment for uncomplicated appendicitis, 4) Timing of appendectomy and in-hospital delay, 5) Surgical treatment 6) Scoring systems for intra-operative grading of appendicitis and their clinical usefulness 7) Non-surgical treatment for complicated appendicitis: abscess or phlegmon 8) Pre-operative and post-operative antibiotics.1

The Mysteries of Trend

Trends are ubiquitous in economic discourse, play a role in much economic theory, and have been intensively studied in econometrics over the last three decades. Yet the empirical economist, forecaster, and policy maker have little guidance from theory about the source and nature of trend behavior, even less guidance about practical formulations, and are heavily reliant on a limited class of stochastic trend, deterministic drift, and structural break models to use in applications. A vast econometric literature has emerged but the nature of trend remains elusive. In spite of being the dominant characteristic in much economic data, having a role in policy assessment that is often vital, and attracting intense academic and popular interest that extends well beyond the subject of economics, trends are little understood. This essay discusses some implications of these limitations, mentions some research opportunities, and briefly illustrates the extent of the difficulties in learning about trend phenomena even when the time series are far longer than those that are available in economics.Climate change, Etymology of trend, Paleoclimatology, Policy, Stochastic trend

Optimal Bandwidth Selection in Heteroskedasticity-Autocorrelation Robust Testing

In time series regressions with nonparametrically autocorrelated errors, it is now standard empirical practice to use kernel-based robust standard errors that involve some smoothing function over the sample autocorrelations. The underlying smoothing parameter b, which can be defined as the ratio of the bandwidth (or truncation lag) to the sample size, is a tuning parameter that plays a key role in determining the asymptotic properties of the standard errors and associated semiparametric tests. Small-b asymptotics involve standard limit theory such as standard normal or chi-squared limits, whereas fixed-b asymptotics typically lead to nonstandard limit distributions involving Brownian bridge functionals. The present paper shows that the nonstandard fixed-b limit distributions of such nonparametrically studentized tests provide more accurate approximations to the finite sample distributions than the standard small-b limit distribution. In particular, using asymptotic expansions of both the finite sample distribution and the nonstandard limit distribution, we confirm that the second-order corrected critical value based on the expansion of the nonstandard limiting distribution is also second-order correct under the standard small-b asymptotics. We further show that, for typical economic time series, the optimal bandwidth that minimizes a weighted average of type I and type II errors is larger by an order of magnitude than the bandwidth that minimizes the asymptotic mean squared error of the corresponding long-run variance estimator. A plug-in procedure for implementing this optimal bandwidth is suggested and simulations confirm that the new plug-in procedure works well in finite samples.Asymptotic expansion, Bandwidth choice, Kernel method, Long-run variance, Loss function, Nonstandard asymptotics, Robust standard error, Type I and Type II errors

Semiparametric Estimation in Time Series of Simultaneous Equations

A system of vector semiparametric nonlinear time series models is studied with possible dependence structures and nonstationarities in the parametric and nonparametric components. The parametric regressors may be endogenous while the nonparametric regressors are strictly exogenous and represent trends. The parametric regressors may be stationary or nonstationary and the nonparametric regressors are nonstationary time series. This framework allows for the nonparametric treatment of stochastic trends and subsumes many practical cases. Semiparametric least squares (SLS) estimation is considered and its asymptotic properties are derived. Due to endogeneity in the parametric regressors, SLS is generally inconsistent for the parametric component and a semiparametric instrumental variable least squares (SIVLS) method is proposed instead. Under certain regularity conditions, the SIVLS estimator of the parametric component is shown to be consistent with a limiting normal distribution that is amenable to inference. The rate of convergence in the parametric component is the usual /n rate and is explained by the fact that the common (nonlinear) trend in the system is eliminated nonparametrically by stochastic detrending.Simultaneous equation, Stochastic detrending, Vector semiparametric regression

Asymptotic Theory for Zero Energy Density Estimation with Nonparametric Regression Applications

A local limit theorem is given for the sample mean of a zero energy function of a nonstationary time series involving twin numerical sequences that pass to infinity. The result is applicable in certain nonparametric kernel density estimation and regression problems where the relevant quantities are functions of both sample size and bandwidth. An interesting outcome of the theory in nonparametric regression is that the linear term is eliminated from the asymptotic bias. In consequence and in contrast to the stationary case, the Nadaraya-Watson estimator has the same limit distribution (to the second order including bias) as the local linear nonparametric estimator.Brownian local time, Cointegration, Integrated process, Local time density estimation, Nonlinear functionals, Nonparametric regression, Unit root, Zero energy functional

Nonlinear Cointegrating Regression under Weak Identification

An asymptotic theory is developed for a weakly identified cointegrating regression model in which the regressor is a nonlinear transformation of an integrated process. Weak identification arises from the presence of a loading coefficient for the nonlinear function that may be close to zero. In that case, standard nonlinear cointegrating limit theory does not provide good approximations to the finite sample distributions of nonlinear least squares estimators, resulting in potentially misleading inference. A new local limit theory is developed that approximates the finite sample distributions of the estimators uniformly well irrespective of the strength of the identification. An important technical component of this theory involves new results showing the uniform weak convergence of sample covariances involving nonlinear functions to mixed normal and stochastic integral limits. Based on these asymptotics, we construct confidence intervals for the loading coefficient and the nonlinear transformation parameter and show that these confidence intervals have correct asymptotic size. As in other cases of nonlinear estimation with integrated processes and unlike stationary process asymptotics, the properties of the nonlinear transformations affect the asymptotics and, in particular, give rise to parameter dependent rates of convergence and differences between the limit results for integrable and asymptotically homogeneous functions.Integrated process, Local time, Nonlinear regression, Uniform weak convergence, Weak identification

Search for the standard model Higgs boson decaying to a bb pair in events with two oppositely-charged leptons using the full CDF data set

We present a search for the standard model Higgs boson produced in association with a Z boson in data collected with the CDF II detector at the Tevatron, corresponding to an integrated luminosity of 9.45/fb. In events consistent with the decay of the Higgs boson to a bottom-quark pair and the Z boson to electron or muon pairs, we set 95% credibility level upper limits on the ZH production cross section times the H -> bb branching ratio as a function of Higgs boson mass. At a Higgs boson mass of 125 GeV/c^2 we observe (expect) a limit of 7.1 (3.9) times the standard model value.Comment: To be submitted to Phys. Rev. Let

Mean and Autocovariance Function Estimation Near the Boundary of Stationarity

We analyze the applicability of standard normal asymptotic theory for linear process models near the boundary of stationarity. The concept of stationarity is refined, allowing for sample size dependence in the array and paying special attention to the rate at which the boundary unit root case is approached using a localizing coefficient around unity. The primary focus of the present paper is on estimation of the the mean, autocovariance and autocorrelation functions within the broad region of stationarity that includes near boundary cases which vary with the sample size. The rate of consistency and the validity of the normal asymptotic approximation for the corresponding estimators is determined both by the sample size n and a parameter measuring the proximity of the model to the unit root boundary. An asymptotic result on the estimation of the localizing coefficient is also presented. To assist in the development of the limit theory in the present case, a suitable asymptotic theory for the behavior of quadratic forms in the vicinity of the boundary of stationarity is provided.Asymptotic normality, Integrated periodogram, Linear process, Local to unity, Localizing coefficient, Moderate deviation, Unit root

Sinusoidal Modeling Applied to Spatially Variant Tropospheric Ozone Air Pollution

This paper demonstrates how parsimonious models of sinusoidal functions can be used to fit spatially variant time series in which there is considerable variation of a periodic type. A typical shortcoming of such tools relates to the difficulty in capturing idiosyncratic variation in periodic models. The strategy developed here addresses this deficiency. While previous work has sought to overcome the shortcoming by augmenting sinusoids with other techniques, the present approach employs station-specific sinusoids to supplement a common regional component, which succeeds in capturing local idiosyncratic behavior in a parsimonious manner. The experiments conducted herein reveal that a semi-parametric approach enables such models to fit spatially varying time series with periodic behavior in a remarkably tight fashion. The methods are applied to a panel data set consisting of hourly air pollution measurements. The augmented sinusoidal models produce an excellent fit to these data at three different levels of spatial detail.Air Pollution, Idiosyncratic component, Regional variation, Semiparametric model, Sinusoidal function, Spatial-temporal data, Tropospheric Ozone