Antibody survey on avian influenza viruses using egg yolks of ducks in Hanoi between 2010 and 2012

In Vietnam, numerous surveillance programs are conducted to monitor the prevalence of avian influenza (AI) viruses. Three serological methods-the agar-gel immunodiffusion test, hemagglutination inhibition (HI) test, and enzyme-linked immunosorbent assay-are well established for detection of AI virus antibodies in poultry sera. Several recent reports have validated egg yolk as an alternative source for detection of AI virus antibodies. In this study, we investigated AI virus antibodies in ducks by HI testing using egg yolk. Ten duck eggs were collected every month from 10 randomly selected markets in Hanoi from April 2010 to March 2012. The HI test was performed using low pathogenic avian influenza (LPAI) viruses (H3, H4, H6, H7, H9, and H11 subtypes) and highly pathogenic avian influenza (HPAI) viruses (H5N1 clade 2.3.4 and 2.3.2.1) as antigens. HI testing for H3, H6, and H9 was 29% positive in November 2010, 50% positive in October and November 2010, and 12% positive in June 2011. These results indicated that several epidemics of LPAI viruses had occurred during the study period. In addition, antibodies against H7 were negative. The results of HI testing for H5N1 showed that the reactivity of the dominant HI antibody shifted from H5N1 clade 2.3.4 to clade 2.3.2.1. In conclusion, egg yolk is useful for long term monitoring of AI virus antibodies and the use of egg-based antibody detection may contribute to improvements in animal welfare

The energy spectrum of all-particle cosmic rays around the knee region observed with the Tibet-III air-shower array

We have already reported the first result on the all-particle spectrum around the knee region based on data from 2000 November to 2001 October observed by the Tibet-III air-shower array. In this paper, we present an updated result using data set collected in the period from 2000 November through 2004 October in a wide range over 3 decades between $10^{14}$ eV and $10^{17}$ eV, in which the position of the knee is clearly seen at around 4 PeV. The spectral index is -2.68 $\pm$ 0.02(stat.) below 1PeV, while it is -3.12 $\pm$ 0.01(stat.) above 4 PeV in the case of QGSJET+HD model, and various systematic errors are under study now.Comment: 12 pages, 7 figures, accepted by Advances in space researc

Moon Shadow by Cosmic Rays under the Influence of Geomagnetic Field and Search for Antiprotons at Multi-TeV Energies

We have observed the shadowing of galactic cosmic ray flux in the direction of the moon, the so-called moon shadow, using the Tibet-III air shower array operating at Yangbajing (4300 m a.s.l.) in Tibet since 1999. Almost all cosmic rays are positively charged; for that reason, they are bent by the geomagnetic field, thereby shifting the moon shadow westward. The cosmic rays will also produce an additional shadow in the eastward direction of the moon if cosmic rays contain negatively charged particles, such as antiprotons, with some fraction. We selected 1.5 x10^{10} air shower events with energy beyond about 3 TeV from the dataset observed by the Tibet-III air shower array and detected the moon shadow at $\sim 40 \sigma$ level. The center of the moon was detected in the direction away from the apparent center of the moon by 0.23$^\circ$ to the west. Based on these data and a full Monte Carlo simulation, we searched for the existence of the shadow produced by antiprotons at the multi-TeV energy region. No evidence of the existence of antiprotons was found in this energy region. We obtained the 90% confidence level upper limit of the flux ratio of antiprotons to protons as 7% at multi-TeV energies.Comment: 13pages,4figures; Accepted for publication in Astroparticle Physic

Are protons still dominant at the knee of the cosmic-ray energy spectrum?

A hybrid experiment consisting of emulsion chambers, burst detectors and the Tibet II air-shower array was carried out at Yangbajing (4,300 m a.s.l., 606 g/cm$^2$) in Tibet to obtain the energy spectra of primary protons and heliums. From three-year operation, these energy spectra are deduced between $10^{15}$ and $10^{16}$ eV by triggering the air showers associated with a high energy core and using a neural network method in the primary mass separation. The proton spectrum can be expressed by a single power-law function with a differential index of $-3.01 \pm 0.11$ and $-3.05 \pm 0.12$ based on the QGSJET+HD and SIBYLL+HD models, respectively, which are steeper than that extrapolated from the direct observations of $-2.74 \pm 0.01$ in the energy range below $10^{14}$ eV. The absolute fluxes of protons and heliums are derived within 30% systematic errors depending on the hadronic interaction models used in Monte Carlo simulation. The result of our experiment suggests that the main component responsible for the change of the power index of the all-particle spectrum around $3 \times 10^{15}$ eV, so-called ``knee'', is composed of nuclei heavier than helium. This is the first measurement of the differential energy spectra of primary protons and heliums by selecting them event by event at the knee energy region.Comment: This paper has been accepted for publication Physics Letters B on October 19th, 2005. This paper has been accepted for publication Physics Letters B on October 19th, 200

Indirect Inference In Fractional Short-term Interest Rate Diffusions

In this article we discuss the estimation of continuous time interest rate models driven by fractional Brownian motion (fBm) using discretely sampled data. In the presence of a fractional Brownian motion, usual estimation methods for continuous time models are not appropriate since in general fBm is neither a semimartingale nor a Markov process. In this context, we discuss the use of simulation-based Indirect Inference. © 2013 IMACS.94109126Aït-Sahalia, Y., Transition densities for interest rate and other nonlinear diffusions (1999) Journal of Finance, 54, pp. 1361-1395Aït-Sahalia, Y., Maximum-likelihood estimation of discretely-sampled diffusions: A closed-form approximation approach (2002) Econometrica, 70, pp. 223-262Andrieu, C., Doucet, A., Holenstein, R., Particle Markov Chain Monte Carlo methods (2010) Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72 (3), pp. 269-342Asmussen, S., (1999) Stochastic Simulation with A View Towards Stochastic Processes, Notes of A Concentrated Advance Course at MaPhySto, , Aarhus DenmarkBachelier, L., Theorie de la speculation, English translation Boness, A.J. (1967) The Random Character of Stock Market Prices, pp. 17-78. , P.H. 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Tech. Rep., , CWI and University of TenesseDuan, J.-C., Jacobs, K., Is long memory necessary? An empirical investigation of nonnegative interest rate processes (2008) Journal of Empirical Finance, 15, pp. 567-5815Flury, T., Shephard, N., (2008) Bayesian Inference Based only on Simulated Likelihood: Particle Filter Analysis of Dynamic Economic Models, , Cambridge University Press CambridgeGallant, G., Tauchen, A.R., (2007) Handbook of Financial Econometrics. 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Forecasting The Term Structure Of Interest Rates Using Integrated Nested Laplace Approximations

This article discusses the use of Bayesian methods for inference and forecasting in dynamic term structure models through integrated nested Laplace approximations (INLA). This method of analytical approximation allows accurate inferences for latent factors, parameters and forecasts in dynamic models with reduced computational cost. In the estimation of dynamic term structure models it also avoids some simplifications in the inference procedures, such as the inefficient two-step ordinary least squares (OLS) estimation. The results obtained in the estimation of the dynamic Nelson-Siegel model indicate that this method performs more accurate out-of-sample forecasts compared to the methods of two-stage estimation by OLS and also Bayesian estimation methods using Markov chain Monte Carlo (MCMC). These analytical approaches also allow efficient calculation of measures of model selection such as generalized cross-validation and marginal likelihood, which may be computationally prohibitive in MCMC estimations. Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd.333214230Bauwens, L., Lubrano, M., Richard, J.-F., (1999) Bayesian Inference in Dynamic Econometric Models, , Cambridge University Press: Cambridge, UKDiebold, F.X., Li, C., Forecasting the term structure of government bond yields (2006) Journal of Econometrics, 130, pp. 337-364Diebold, F.X., Rudebusch, G.D., Aruoba, S.B., The macroeconomy and the yield curve: A dynamic latent factor approach (2006) Journal of Econometrics, 131, pp. 309-338Duffie, D., Kan, R., A yield-factor model of interest rates (1996) Mathematical Finance, 6, pp. 379-406Fama, E., Bliss, R.R., The information in long-maturity forward rates (1987) American Economic Review, 77, pp. 680-691Geweke, J., (2010) Complete and Incomplete Econometric Models, , Princeton University Press: Princeton, NJGeweke, J., Amisano, G., Comparing and evaluating Bayesian predictive distributions of asset returns (2010) International Journal of Forecasting, 26 (2), pp. 216-230Hautsch, N., Yang, F., Bayesian inference in a stochastic volatility Nelson-Siegel model (2012) Computational Statistics and Data Analysis, 56 (11), pp. 3774-3792Held, L., Schrödle, B., Rue, H., Posterior and cross-validatory predictive checks: A comparison of MCMC and INLA (2010) Statistical Modelling and Regression Structures: Festschrift in Honour of Ludwig Fahrmeir, pp. 91-110. , Kneib T. Tutz G. (eds). Springer: BerlinKim, D.H., Orphanides, A., (2005) Term Structure Estimation with Survey Data on Interest Rate Forecasts, , Finance and Economics Discussion Series, 2005-08, Board of Directors of Federal Reserve SystemKoopman, S.J., Mallee, M.I.P., Van Der Wel, M., Analyzing the term structure of interest rates using the dynamic Nelson-Siegel model with time-varying parameters (2010) Journal of Business and Economic Statistics, 28, pp. 329-343Lahiri, K., Martin, G., Special issue: Bayesian forecasting in economics (2010) International Journal of Forecasting, 26 (2), pp. 211-444Laurini, M.P., A hybrid data cloning maximum likelihood estimator for stochastic volatility models (2013) Journal of Time Series Econometrics, 5 (2), pp. 193-229Laurini, M.P., Hotta, L.K., Bayesian extensions to Diebold-Li term structure model (2010) International Review of Financial Analysis, 19 (5), pp. 342-350Migon, H., Abanto-Valle, C., A Bayesian term structure modeling (2007) Proceedings of the Third Brazilian Conference on Statistical Modelling in Insurance and Finance: IME-USP, pp. 200-203Nelson, C.R., Siegel, A.F., Parsimonious modelling of yield curves (1987) Journal of Business, 60 (4), pp. 473-489Newton, M.A., Raftery, A.E., Approximate Bayesian inference by the weighted likelihood bootstrap (with discussion) (1994) Journal of the Royal Statistical Society B, 56, pp. 3-48Rue, H., Martino, S., Chopin, N., Approximated Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (with discussion) (2009) Journal of the Royal Statistical Society B, 71, pp. 319-392Ruiz-Cárdenas, R., Krainski, E.T., Rue, H., Direct fitting of dynamic models using integrated nested Laplace approximations (2012) Computational Statistics and Data Analysis, 56 (6), pp. 1808-1828Wang, W., Model selection (2004) Handbook of Computational Statistics, pp. 469-498. , Springer: BerlinYu, W.-C., Zivot, E., Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models (2011) International Journal of Forecasting, 27 (2), pp. 579-59

Slope Influence Diagnostics In Conditional Heteroscedastic Time Series Models

In this paper, we provide useful and simple expressions for slope influence diagnostics of several conditional heteroscedastic time series models under innovative model perturbations. These expressions are obtained by establishing a connection between the local influence and residual diagnostics. Monte Carlo experiments provided good results in terms of the size and power of the proposed statistics. To illustrate the results, we analyze the financial time series returns of the S&P500 and DJIA indexes.2913452Abraham, B., Yatawara, N., A score test for detection of time series outliers (1988) Journal of Time Series Analysis, 9, pp. 109-119. , MR0943001Billor, N., Loynes, R.M., Local influence: A new approach (1993) Communication in Statistics-Theory and Methods, 22, pp. 1595-1611. , MR1224984Bollerslev, T., Generalized autoregressive conditional heteroskedasticity (1986) Journal of Econometrics, 31, pp. 307-327. , MR0853051Charles, A., Darné, O., Outliers and GARCH models in financial data (2005) Economic Letters, 86, pp. 347-352. , MR2124418Cook, R.D., Assessment of local influence (with discussion) (1986) Journal of the Royal Statistical Society B, 48, pp. 133-169. , MR0867994Ding, Z., Granger, C.W.J., Engle, R.F., A long memory property of stock market returns and a new model (1993) Journal of Empirical Finance, 1, pp. 83-106Doornik, J.A., Ooms, M., (2005) Outlier detection in GARCH models, , Tinbergen Institute discussion paper TI 2005-092/4, Vrije Universiteit AmsterdamEngle, R., New frontiers for ARCH models (2002) Journal of Applied Econometrics, 17, pp. 425-446Franses, P.H., van Dijk, D., (1999) Outlier detection in the GARCH(1, 1) model, , Econometric Institute Research Report EI-9926/A, Erasmus Univ. RotterdamFranses, P.H., van Dijk, D., (2000) Non-Linear Time Series Models in Empirical Finance, , Cambridge, UK: Cambridge Univ. PressGlosten, L., Jagannathan, R., Runkle, D., On the relation between expected value and the volatility of the nominal excess returns on stocks (1993) Journal of Finance, 48, pp. 1779-1801Hotta, L.K., Tsay, R., Outliers in GARCH Processes (2012) Economic Time Series: Modeling and Seasonality, pp. 337-358. , (W.R. Bell, S.H. Holan and T.S. McElroy, eds.) Boca Raton, FL: Chapman & Hall/CRC Press. MR3076022Johnson, N.L., Kotz, S., Balakrishnan, N., (1995) Continuous Univariate Distributions, , 2nd ed. New York: Wiley. MR1326603Leadbetter, M.R., Extremes and local dependence in stationary sequences (1983) Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, 65, pp. 291-306. , MR0722133Liu, S., On diagnostics in conditionally heteroskedastic time series models under elliptical distributions (2004) Journal of Applied Probability, 41 A, pp. 393-405. , MR2057589Nelson, D.B., Conditional heteroskedasticity in asset returns: A new approach (1991) Econometrica, 59, pp. 347-370. , MR1097532Schwarzmann, B., A connection between local-influence analysis and residual diagnostics (1991) Technometrics, 33, pp. 103-104Zevallos, M., Santos, B., Hotta, L.K., A note on influential diagnostics in AR(1) time series models (2012) Journal of Statistical Planning and Inference, 142, pp. 2999-3007. , MR2943772Zevallos, M., Hotta, L.K., Influential observations in GARCH models (2012) Journal of Statistical Computation and Simulation, 82, pp. 1571-1589. , MR2984562Zhang, X., King, M.L., Influence diagnostics in generalized autoregressive conditional heteroscedasticity processes (2005) Journal of Business & Economic Statistics, 23, pp. 118-129. , MR2108697Zivot, E., Wang, J., (2006) Modeling Financial Time Series with S-PLUS, , 2nd ed. New York: Springer. MR200094

Control Of Schistosomiasis Mansoni In An Area Of Low Transmission [controle Da Esquistossomose Mansônica Em área De Baixa Transmissão.]

The schistosomiasis is transmitted by Biomphalaria tenagophila in our study area (Pedro de Toledo, São Paulo, Brazil). From 1980 to 1990 epidemiological surveys in a population of 4,000 inhabitants, has shown that: prevalences by Kato-Katz (KKT), immunofluorescence (FT) and intradermal (IDT) techniques were 22.8%, 55.5% and 51.8%, respectively; intensity of infection was low, 58.5 eggs per gram of faeces (epg); there were no symptomatic cases; prevalences were higher in mates, children and rural zone; index of potential contamination was 57.5% in the age group 5 to 20 years; 2/3 of patients were autochthonous; cases were no-randomly aggregated; transmission was focal and only 0.4% of snails were infected; water contacts through recreation showed the most important odds ratio; knowledge, attitudes and practices were satisfactory. From the epidemiological findings a control programme was carried out: yearly faeces exams, chemotherapy, molluscocide, health education and sanitation. Thus, the prevalence decreased sharply to 3.3% and intensity of infection to 30.3 epg; the incidence rates ranged between 0.4% and 2.5% annually; the sanitation became better and the youngsters were the main target in prophylaxis. To improve control, immunodiagnosis has to be conducted and the involvement of the population should be increase. However, we cannot forget that re-infection, therapeutic failure, etc, could play a major role in the maintenance this residual prevalence.87 Suppl 423323