bluuebunny/crossref_metadata_2025 huggingface dataset:
ASYMPTOTICS FOR THE LOW‐FREQUENCY ORDINATES OF THE PERIODOGRAM OF A LONG‐MEMORY TIME SERIES
Authors: Hurvich Clifford M. , Beltrao Kaizo I.
Published: May 2008

Abstract. We consider the asymptotic distribution of the normalized periodogram ordinates I(ωj)/f(ωj) (j= 1,2,…) of a general long‐memory time series. Here, I(ω;) is the periodogram based on a sample size n, f(ω) is the spectral density and ωj= 2πj/n. We assume that n→∝ with j held fixed, and so our focus is on low frequencies; these are the most important frequencies for the periodogram‐based estimation of the memory parameter d. Contrary to popular belief, the normalized periodogram ordinates obtained from a Gaussian process are asymptotically neither independent identically distributed nor exponentially distributed. In fact, limn E{I(ωj)/f(ωj)} depends on both j and d and is typically greater than unity, implying a positive asymptotic relative bias in I(ωj) as an estimator of f(ωj). Tapering is found to reduce this bias dramatically, except at frequency ω1. The asymptotic distribution of I(ωj)/f(ωj) for a Gaussian process is, in general, that of an unequally weighted linear combination of two independent X21 random variables. The asymptotic mean of the log normalized periodogram depends on j and d and is not in general equal to the negative of Euler's constant, as is commonly assumed. Consequently, the regression estimator of d proposed by Geweke and Porter‐Hudak will be asymptotically biased if the number of frequencies used in the regression is held fixed as n→∝.
TRANSFER FUNCTION ESTIMATION
Authors: Kavalieris L.
Published: May 2008

Abstract.We discuss the problem of estimating the transfer function and an autoregressive model for measurement noise in a linear system. A parameterization that is often more parsimonious than the usual ARX model is used. Particular emphasis is given to the selection of model orders when it is not assumed that the true system is finitely parameterized. Consistency of the transfer function and noise spectrum estimates is established using a new uniform convergence law for an appropriate class of martingale transforms.
MODELING LONG‐MEMORY PROCESSES FOR OPTIMAL LONG‐RANGE PREDICTION
Authors: Ray Bonnie K.
Published: May 2008

Abstract. We look at the implications of modeling observations from a fractionally differenced noise process using an approximating AR (p) model. The approximation is used because of computational difficulties in the estimation of the differencing parameter of the fractional noise model. Because the fractional noise process is long‐range dependent, we assess the applicability of the approximating autoregressive (AR) model based on its long‐range forecasting accuracy compared with that of the fractional noise model. We derive the asymptotic k‐step‐ahead prediction error for a fractional noise process modeled as an AR(p) process and compare it with the k‐step‐ahead prediction error obtained when the model for the observed series is correctly specified as a fractional noise process and the fractional differencing parameter d is either known or estimated. We also assess the validity of the asymptotic results for a finite sample size via simulation. We see that AR models can be useful for long‐range forecasting of long‐memory data, provided that consideration is given to the forecast horizon when choosing an approximating model.
CONTINUOUS‐TIME DYNAMICAL SYSTEMS WITH SAMPLED DATA, ERRORS OF MEASUREMENT AND UNOBSERVED COMPONENTS
Authors: Singer Hermann
Published: May 2008

Abstract.Maximum likelihood estimation of sampled continuous‐time stochastic processes is considered. The likelihood is directly maximized with respect to the original structural parameters using a scoring algorithm with exact analytical derivatives. Furthermore, the case of unobserved states and errors of measurement is treated via EM and quasi‐Newton algorithms. The proposed methods are illustrated with simulation studies and analysis of sunspot activity.
THE DETERMINATION OF THE NUMBER OF TERMS IN A MULTICHANNEL SINUSOIDAL REGRESSION
Authors: Sakai Hideaki
Published: May 2008

Abstract. This paper treats a problem in multichannel harmonic analysis when the signals are sums of sinusoids whose periods are divisors of the series length and the disturbance noises are white Gaussian. The presence or absence of the harmonics is determined by an information‐criterion‐like method. The criterion proposed here is a multichannel generalization of a Bayesian information criterion for the scalar signal case independently derived by Quinn and the author. The criterion is based on the distribution of the maximum of N/2 independent χ2d7 random variables where N is the series length and d is the number of channels. Some simulation results are also presented.
FORECASTING OF MULTIVARIATE PERIODIC AUTOREGRESSIVE MOVING‐AVERAGE PROCESSES
Authors: Ula Taylan A.
Published: May 2008

Abstract.Minimum mean square error forecasting of multivariate autoregressive moving‐average processes with periodically varying parameters and orders is considered. General expressions are obtained for the forecasts, their errors and the covariance matrices of the forecast errors. Recursive evaluations of these quantities are shown to follow from the conditional expectation approach. Prediction ellipsoids and intervals for future values of the process are given. Update equations for the forecasts are obtained. The general results are illustrated and verified for a particular case of the process. A simulated example is given.
A GENERAL METHOD FOR ESTIMATING THE VARIANCES OF X‐11 SEASONALLY ADJUSTED ESTIMATORS
Authors: Pfeffermann D.
Published: May 2008

Abstract.The X‐11 procedure with its various variants is the commonly used procedure for seasonal adjustment throughout the world. A well‐known problem with the use of this procedure, however, is the estimation of the variances of its output such as, for example, the variances of the seasonally adjusted data or the month to month changes in these data. In this paper we propose a simple general procedure for estimating the variances of the X‐11 estimators. The variances account for the sampling distribution of the survey estimators around the corresponding population values and for the distribution of the component series included in the decomposition of the population values. The procedure is applicable to general sampling designs, including partially overlapping surveys. Empirical results illustrating the performance of the procedure when applied to simulated and real series are presented.
BAYESIAN ANALYSIS OF AUTOREGRESSIVE TIME SERIES VIA THE GIBBS SAMPLER
Authors: McCulloch Robert E. , Tsay Ruey S.
Published: May 2008

Abstract. Applications of the Gibbs Sampler in time series analysis are considered. We show that the sampler applies nicely to various problems in analyzing autoregressive processes and, in many cases, it enjoys certain advantages over the traditional methods. The problems considered include random level‐shift models, outliers and missing values. Real examples are used to illustrate the analysis.
ORDER IDENTIFICATION IN MISSPECIFIED AUTOREGRESSIVE TIME SERIES MODELS
Authors: Hall Alastair
Published: May 2008

Abstract.In this paper we examine the properties of Akaike's (Fitting autoregressive models for prediction,Ann. Inst. Statist. Math.21 (1969), 243–47) and Hannan and Quinn's (The determination of the order of an autoregression,J. R. Statist. Soc., Ser. B41 (1979), 190–95) information criteria in stationary autoregressive time series models when the true order of the process is greater than the maximum considered by the analyst. The limiting distributions of the estimated orders are derived and the implications of these results for model building are considered.
PEAK‐INSENSITIVE NON‐PARAMETRIC SPECTRUM ESTIMATION
Authors: Sachs Rainer von
Published: May 2008

Abstract.We study the problem of non‐parametric spectrum estimation of a stationary time series that might contain periodic components. In this case the periodogram ordinates have a significant amplitude at frequencies near the frequencies of the periodic components. These can be regarded as outliers in an asymptotically exponential sample. We develop a non‐parametric estimator for the spectral density that is insensitive to these outliers in the frequency domain. This is done by robustifying the usual kernel estimator (smoothed periodogram) by means of M‐estimation in the frequency domain. We propose to use data‐tapered periodograms, which yield a drastic improvement of the procedure, typically for the contaminated situation. This is both shown theoretically and supported by means of simulation. We show consistency of the resulting estimator in the general case, and asymptotic normality in the special case of a Gaussian time series, whether contamination is present or not. Finally we illustrate the finite sample performance of the estimating procedure by some simulation results and by application to the Canadian lynx trappings data.
LEAST SQUARES ESTIMATION OF A SHIFT IN LINEAR PROCESSES
Authors: Bai Jushan
Published: May 2008

Abstract. This paper considers a mean shift with an unknown shift point in a linear process and estimates the unknown shift point (change point) by the method of least squares. Pre‐shift and post‐shift means are estimated concurrently with the change point. The consistency and the rate of convergence for the estimated change point are established. The asymptotic distribution for the change point estimator is obtained when the magnitude of shift is small. It is shown that serial correlation affects the variance of the change point estimator via the sum of the coefficients of the linear process. When the underlying process is autoregressive moving average, a mean shift causes overestimation of its order. A simple procedure is suggested to mitigate the bias in order estimation.
ON THE MAXIMUM ENTROPY PROPERTY OF NONLINEAR AUTOREGRESSIONS
Authors: Politis Dimitris N.
Published: May 2008

Abstract. The assumption of a linear autoregressive model for time series has often been justified on the basis of a maximum entropy principle. The purpose of this short note is to point out that the class of nonlinear autoregressions is also characterized by a maximum entropy property.
COINTEGRATION AND COMMON FACTORS
Authors: Escribano Alvaro , Peña Daniel
Published: May 2008

Abstract. Alternative common factor representations for cointegrated vectors are studied. This is done by embedding them into the dynamic factor model proposed by Peña and Box (Identifying a simplifying structure in time series. J. Am. Statist. Assoc. 82 (1987), 836–43). It is shown that dynamic factor models produce as a particular case the alternative common trend representations for cointegrated variables available in the literature. Furthermore a new normalization is proposed which has the advantage of producing common trend representations with moving‐average polynomials and under certain circumstances with uncorrelated shocks.
ESTIMATION OF FRACTAL INDEX AND FRACTAL DIMENSION OF A GAUSSIAN PROCESS BY COUNTING THE NUMBER OF LEVEL CROSSINGS
Authors: Feuerverger Andrey , Hall Peter , Wood Andrew T. A.
Published: May 2008

Abstract.The fractal index α and fractal dimensionDof a Gaussian process are characteristics that describe the smoothness of the process. In principle, smoother processes have fewer crossings of a given level, and so level crossings might be employed to estimate α orD.However, the number of crossings of a level by a non‐differentiable Gaussian process is either zero or infinity, with probability one, so that level crossings are not directly usable. Crossing counts may be rendered finite by smoothing the process. Therefore, we consider estimators that are based on comparing the sizes of the average numbers of crossings for a small, bounded number of different values of the smoothing bandwidth. The averaging here is over values of the level. Strikingly, we show that such estimators are consistent, as the size of the smoothing bandwidths shrinks to zero, if and only if the weight function in the definition of ‘average’ is constant. In this important case we derive the asymptotic bias and variance of the estimators, assuming only a non‐parametric description of covariance, and describe the estimators' numerical properties. We also introduce a novel approach to generating Gaussian process data on a very fine grid.
DETERMINING THE NUMBER OF TERMS IN A TRIGONOMETRIC REGRESSION
Authors: Kavalieris L. , Hannan E. J.
Published: May 2008

Abstract. We consider the estimation of the number of sinusoidal terms in a time series contaminated by additive noise with unknown correlation structure. The method fits sinusoidal terms by least squares and models the noise component using a high order autoregression. A criterion based on the minimum description length principle is used to select the number of sinusoidal terms and the order of the noise model. The small sample efficacy of the model selection procedure is examined by simulations and the analysis of some astronomical data. Consistency is proved under quite general conditions on the noise spectrum.
ON THE SQUARED RESIDUAL AUTOCORRELATIONS IN NON‐LINEAR TIME SERIES WITH CONDITIONAL HETEROSKEDASTICITY
Authors: Li W. K. , Mak T. K.
Published: May 2008

Abstract. Time series with a changing conditional variance have been found useful in many applications. Residual autocorrelations from traditional autoregressive moving‐average models have been found useful in model diagnostic checking. By analogy, squared residual autocorrelations from fitted conditional heteroskedastic time series models would be useful in checking the adequacy of such models. In this paper, a general class of squared residual autocorrelations is defined and their asymptotic distribution is obtained. The result leads to some useful diagnostic tools for statisticians using conditional heteroskedastic time series models. Some simulation results and an illustrative example are also reported.
BISPECTRAL ANALYSIS OF RANDOMLY SAMPLED DATA
Authors: Lii Keh‐Shin , Tsou Tai‐Houn
Published: May 2008

Abstract.A continuous time series is often observed or sampled at discrete intervals. Most literature has dealt with the case when the sampling intervals are equally spaced. For irregularly sampled data, most existing literature is concerned with second‐order moments or anti‐aliasing spectral estimations. We study the estimation of higher‐order spectral density functions with the emphasis on the bispectral estimate when the continuous time series is sampled by a random point process. Estimates under the Poisson sampling scheme are studied in detail. Asymptotic bias and covariances are obtained. In particular, it is shown explicitly how the information of the sampling process comes into play in obtaining a consistent estimate of the bispectral density function of a continuous time series. In contrast to the second‐order spectral density function estimation where the Poisson sampling scheme results in a constant correction term, a consistent bispectral density function estimate results in a nonlinear correction term even in the Poisson sampling scheme. A simple simulation example is presented for illustration.
AN ALGORITHM FOR A PERIOD SEARCH IN A SPARSELY COVERED TIME SERIES AT A FIXED PHASE
Authors: Leibowitz Daniela , Leibowitz Elia M.
Published: May 2008

Abstract. Researchers are sometimes faced with a set of observations that constitute a sparse coverage of a time series, suspected to be periodic. The period is unknown, but one can identify in the observed data a few points that are presumed to occur at the same phase, in different cycles of the unknown periodicity. We propose an algorithm that finds all periods which are compatible with such observed data, and suggest how to assess their statistical significance. The algorithm also provides stringent limits on the epochs of the fixed phase. We give three examples, from the field of astronomy, for application of our new algorithm. In the first one the algorithm reveals, on the basis of very few photometric observations, a highly significant period in the light curve of the recent classical Nova Herculis 1991. In the second example, in the series of arrival times of neutrinos from the supernova SN1987A, our algorithm yields a definite negative result. It proves that no significant exact periodicity is present in the data. In the third application, the algorithm provides new constraints on the epoch of one of the minima in the light curve of the stellar binary system 44i Bootis. We compare the method with other period search techniques, pointing out a few of its advantages, as well as some of its weaknesses.
A GENERALIZED VARIANCE RATIO TEST OF ARIMA (p, 1, q) MODEL SPECIFICATION
Authors: Miller John P. , Newbold Paul
Published: May 2008

Abstract. The variance ratio test is often used as a check of the hypothesis that a time series is generated by a random walk. A natural extension of the test is developed to cover the case where the assumed model is ARIMA(p, 1, q), with unknown parameters. Small sample properties of the generalized test are investigated, and the test is applied to a frequently analysed data set on US quarterly real gross national product. In effect, we are testing for low frequency misspecification in assumed autoregressive moving‐average (ARMA) models for a differenced series.
ALTERNATIVE ESTIMATORS AND UNIT ROOT TESTS FOR THE AUTOREGRESSIVE PROCESS
Authors: Park Heon Jin , Fuller Wayne A.
Published: May 2008

Abstract. We compare several estimators for the second‐order autoregressive process and compare the associated tests for a unit root. Monte Carlo results are reported for the ordinary least squares estimator, the simple symmetric least squares estimator and the weighted symmetric least squares estimator. The weighted symmetric least squares estimator of the autoregressive parameters generally has smaller mean square error than that of the ordinary least squares estimator, particularly when one root is close to one in absolute value. For the second‐order model with known zero intercept, the one‐sided ordinary least squares test for a unit root is more powerful than the symmetric tests. For the model with an estimated intercept, the one‐sided weighted symmetric least squares test is the most powerful test.
BAYESIAN INFERENCE OF THRESHOLD AUTOREGRESSIVE MODELS
Authors: Chen Cathy W. S. , Lee Jack C.
Published: May 2008

Abstract.The study of non‐linear time series has attracted much attention in recent years. Among the models proposed, the threshold autoregressive (TAR) model and bilinear model are perhaps the most popular ones in the literature. However, the TAR model has not been widely used in practice due to the difficulty in identifying the threshold variable and in estimating the associated threshold value. The main focal point of this paper is a Bayesian analysis of the TAR model with two regimes. The desired marginal posterior densities of the threshold value and other parameters are obtained via the Gibbs sampler. This approach avoids sophisticated analytical and numerical multiple integration. It also provides an estimate of the threshold value directly without resorting to a subjective choice from various scatterplots. We illustrate the proposed methodology by using simulation experiments and analysis of a real data set.
ESTIMATING FINITE SAMPLE CRITICAL VALUES FOR UNIT ROOT TESTS USING PURE RANDOM WALK PROCESSES:A NOTE
Authors: Cheung Yin‐Wong , Lai Kon S.
Published: May 2008

Abstract. Finite sample critical values currently available for the augmented Dickey‐Fuller test are commonly obtained via simulations using ARIMA (0, 1, 0) processes. An implicit but critical assumption is that the possible presence of nuisance nonunit roots in general processes does not affect the finite sample size property of the test. The validity of this assumption, though always presumed, has not been verified. This study shows that the use of ARIMA (0, 1, 0) processes for computing the critical values is not so restrictive as it may seem. By estimating empirical size curves as a function of nuisance root parameters, results of Monte Carlo analysis suggest that the empirical test size is not sensitive to nuisance autoregressive (AR) and moving‐average (MA) roots over a wide range of their values, except only when the AR or MA root is near unity. The results support, though not unqualifiedly, the reliability and usefulness of finite sample critical values estimated based on simple ARIMA (0, 1, 0) processes.
RESIDUAL AUTOCOVARIANCES AND UNIT ROOT TESTS BASED ON INSTRUMENTAL VARIABLE ESTIMATORS FROM TIME SERIES REGRESSION MODELS
Authors: Hall Alastair
Published: May 2008

Abstract. Hall (Testing for a unit root in the presence of moving average errors. Biometrika 76 (1989), 49–56; Joint hypothesis tests for a random walk based on instrumental variable estimators. J. Time Ser. Anal. 13 (1992), 29–45), Pantula and Hall (Testing for unit roots in autoregressive moving average models:an instrumental variable approach. J. Econometrics 48 (1991), 325–53) and Lee and Schmidt (Unit root tests based on instrumental variable estimation. Int. Econ. Rev. 39 (1994), 449–62) proposed instrumental variable (IV) based tests for a unit root in an ARMA(p+ 1, q) time series. To perform the tests it is essentially necessary to know (p, q) but in many cases this information is unknown. In practice a natural solution to this problem is to estimate (p, q) from the data using a strategy based on the residual autocovariances from the IV regression. In this paper we examine the properties of these residual autocovariances under various assumptions about the true nature of the time series. This analysis allows us to propose a model selection procedure which has desirable asymptotic and finite sample properties whether the time series is stationary or possesses a unit root. A sideproduct of our analysis is that we extend Box and Pierce's (Distribution of residual autocorrelations in autoregressive integrated moving average time series models. J. Am. Statist. Assoc. 65 (1970), 1509–26) analysis of the least squares residual autocorrelations to the residual autocovariances from IV regressions.
CONSISTENCY FOR NON‐LINEAR FUNCTIONS OF THE PERIODOGRAM OF TAPERED DATA
Authors: Janas Daniel , Sachs Rainer von
Published: May 2008

Abstract. In this paper we investigate the merits of using a data taper in non‐linear functional of the periodogram of a stationary time series. To this end, we show consistency for a general class of statistics of the form , where A(ω) is a function of bounded variation and where φ is allowed to be a non‐linear function of the periodogram IT(ω) of the tapered data. The key step in deriving our asymptotic results is an Edgeworth expansion for the finite Fourier transform of the tapered data, which do not have to follow a particular distribution (i.e. we allow for non‐Gaussianity). Important applications are the estimation of , choosing φ to be a suitable transform of a given function g (see Taniguchi, On estimation of the integrals of certain functions of spectral density. J. Appl. Prob. 17 (1980). 73–83), the peak‐insensitive spectrum estimator of von Sachs (Peak‐insensitive nonparametric spectrum estimation. J. Time Ser. Anal. 15 (1994), 429–52), where φ is chosen to be a bounded (robustifying) σ function, and the parametric approach of Chiu (Peak‐insensitive parametric spectrum estimation. Stoch. Proc. Appl. 35 (1990). 121–40) on robust estimation of the parameters of the continuous spectrum of the time series.
EXACT MAXIMUM LIKELIHOOD ESTIMATION IN AUTOREGRESSIVE PROCESSES
Authors: Miller James W.
Published: May 2008

Abstract. The purpose of this paper is to complement the theory of exact maximum likelihood estimation in pure autoregressive processes by differentiating the exact Gaussian likelihood function with respect to the model parameters and obtaining a set of likelihood equations very similar in form to the Yule—Walker equations. The main contribution of this paper is a very simple expression for the derivatives and the resulting likelihood equations in terms of the components of a (p+ 1) x (p+ 1) function of the data, the model parameters (s̀2, φ) and the autocovariances at lags 0 through p. We propose an iterative algorithm for solving the likelihood equations by alternately solving two linear systems, first for (s̀2, φ) given current estimates of the autocovariances, then for updated estimates of the autocovariances given current estimates of (s̀2, φ). The number of operations per iteration is independent of the series length since the algorithm uses the data only through the value of the (p+ 1) x (p+ 1) sufficient statistic.
ASYMPTOTIC INFERENCE FOR NON‐INVERTIBLE MOVING‐AVERAGE TIME SERIES
Authors: Chan Ngai Hang , Tsay Ruey S.
Published: May 2008

Abstract. This paper is concerned with statistical inference of nonstationary and non‐invertible autoregressive moving‐average (ARMA) processes. It makes use of the fact that a derived process of an ARMA(p, q) model follows an AR(q) model with an autoregressive (AR) operator equivalent to the moving‐average (MA) part of the original ARMA model. Asymptotic distributions of least squares estimates of MA parameters based on a constructed derived process are obtained as corresponding analogs of a nonstationary AR process. Extensions to the nearly non‐invertible models are considered and the limiting distributions are obtained as functionals of stochastic integrals of Brownian motions and Ornstein‐Uhlenbeck processes. For application, a two‐stage procedure is proposed for testing unit roots in the MA polynomial. Examples are given to illustrate the application.
RELATIVE POWER OF t TYPE TESTS FOR STATIONARY AND UNIT ROOT PROCESSES
Authors: Gonzalo Jesus , Lee Tae‐Hwy
Published: May 2008

Abstract. This paper shows numerically that the lack of power and size distortions of the Dickey‐Fuller type tests for unit roots (very well documented in the unit root literature) are similar to and in many situations even smaller than the lack of power and size distortions of the standard Student t tests for stationary roots of an autoregressive model.
WAVELETS AND TIME‐DEPENDENT SPECTRAL ANALYSIS
Authors: Priestley M. B.
Published: May 2008

Abstract. One of the key features of wavelet analysis is its potential use for effecting time‐frequency decompositions of non‐stationary signals. The relationship between wavelet analysis and time‐dependent spectral analysis has so far rested mainly on heuristic reasoning:in this paper we examine the relationship in a more precise mathematical form. A crucial feature of this analysis is the need to define carefully the notion of “frequency” when applied to non‐stationary signals.
DISTRIBUTION OF RESIDUAL AUTOCORRELATIONS IN NONSTATIONARY AUTOREGRESSIVE PROCESSES
Authors: Shin Dong Wan , Lee Jong Hyup
Published: May 2008

Abstract.The residual autocorrelations in nonstationary autoregressive processes with autoregressive characteristic roots on the unit circle are considered. Limiting distributions of the residual autocovariances and the residual autocorrelations are shown to be the same as the limiting distributions when parameters are estimated with all roots on the unit circle known. The portmanteau statistic is shown to have a x2limiting distribution. The Canadian lynx data set is analysed to illustrate our theory. The portmanteau test seems also useful when the characteristic roots are close to the unit circle.
A BAYESIAN APPROACH TO ESTIMATING AND FORECASTING ADDITIVE NONPARAMETRIC AUTOREGRESSIVE MODELS
Authors: Wong Chi‐ming , Kohn Robert
Published: May 2008

Abstract.We present a Bayesian approach for estimating nonparametrically an additive autoregressive model with the regression curve estimates cubic smoothing splines. Our approach is robust to innovation outliers; it can handle missing observations and produce multistep ahead forecasts. The computation is carried out using Markov chain Monte Carlo and requires O(nM) operations wherenis the sample size andMis the number of Markov chain iterations. This makes it the first exact algorithm for spline smoothing of an additive autoregressive model which can handle large data sets. The properties of the estimates and forecasts are studied empirically using simulated and real data sets.
UNIT ROOTS IN PERIODIC AUTOREGRESSIONS
Authors: Boswijk H. Peter , Franses Philip Hans
Published: May 2008

Abstract. This paper analyzes the presence and consequences of a unit root in periodic autoregressive models for univariate quarterly time series. First, we consider various representations of such models, including a new parametrization which facilitates imposing a unit root restriction. Next, we propose a class of likelihood ratio tests for a unit root, and we derive their asymptotic null distributions. Likelihood ratio tests for periodic parameter variation are also proposed. Finally, we analyze the impact on unit root inference of misspecifying a periodic process by a constant‐parameter model.
TESTING FOR A UNIT ROOT IN AN AR(1) TIME SERIES USING IRREGULARLY OBSERVED DATA
Authors: Shin Dong Wan , Sarkar Sahadeb
Published: May 2008

Abstract.For an AR(1) model having a unit root with nonconsecutively observed or missing data we consider the ordinary least squares estimator, the one‐step Newton‐Raphson estimator and an ordinary least squares type estimator which is a simple approximation of the Newton‐Raphson estimator. It is shown that the limiting distributions of these estimators of the unit root are the same as those of the regression estimators as tabulated by Dickey and Fuller (Distribution of the estimators for autoregressive time series with a unit root.J. Am. Statist. Assoc.74 (1979), 427–31) for the complete data situation. Simulation results show that our proposed unit root tests perform very well for small samples.
HIGHER ORDER MOMENTS OF SAMPLE AUTOCOVARIANCES AND SAMPLE AUTOCORRELATIONS FROM AN INDEPENDENT TIME SERIES
Authors: Anderson Oliver D. , Chen Zhao‐Guo
Published: May 2008

Abstract.Given length‐nsampled time series, generated by an independent distributed process, in this paper we treat the problem of determining the greatest order, inn, that moments of the sample autocovariances and sample autocorrelations can attain. For the sample autocovariance moments, we achieve quite general results; but, for the autocorrelation moments, we restrict study to Gaussian white noise (normal, independent and identically distributed). Our main theorem relates to the cross‐moments of the non‐centred sample autocovariances, but we also establish a relation between these and the corresponding moments for the centred sample autocovariances.
AN OUTLIER TEST FOR TIME SERIES BASED ON A TWO‐SIDED PREDICTOR
Authors: Schmid W.
Published: May 2008

Abstract.In this paper an outlier test for contaminated autoregressive processes is introduced. The test is based on a comparison of each observation with a predictor using past and future values, a so‐called two‐sided predictor. It is required that an upper bound for the total number of outliers is known.

The asymptotic distribution of the test statistic is calculated under the null hypothesis that no outlier is present. The behaviour of the test for finite sample size is investigated by a simulation study. Moreover, the test is compared with several other outlier tests.
MULTIVARIATE LOCAL POLYNOMIAL REGRESSION FOR TIME SERIES:UNIFORM STRONG CONSISTENCY AND RATES
Authors: Masry Elias
Published: May 2008

Abstract. Local high‐order polynomial fitting is employed for the estimation of the multivariate regression function m(x1,…xd) =E{φ(Yd)φX1=x1,…,Xd=xd}, and of its partial derivatives, for stationary random processes {Yi, Xi}. The function φ may be selected to yield estimates of the conditional mean, conditional moments and conditional distributions. Uniform strong consistency over compact subsets of Rd, along with rates, are established for the regression function and its partial derivatives for strongly mixing processes.
SPECTRAL DENSITY ESTIMATION VIA NONLINEAR WAVELET METHODS FOR STATIONARY NON‐GAUSSIAN TIME SERIES
Authors: Neumann Michael H.
Published: May 2008

Abstract.In the present paper we consider nonlinear wavelet estimators of the spectral densityfof a zero mean, not necessarily Gaussian, stochastic process, which is stationary in the wide sense. It is known in the case of Gaussian regression that these estimators outperform traditional linear methods if the degree of smoothness of the regression function varies considerably over the interval of interest. Such methods are based on a nonlinear treatment of empirical coefficients that arise from an orthonormal series expansion according to a wavelet basis.

The main goal of this paper is to transfer these methods to spectral density estimation. This is done by showing the asymptotic normality of certain empirical coefficients based on the tapered periodogram. Using these results we can show the risk equivalence to the Gaussian case for monotone estimators based on such empirical coefficients. The resulting estimator offkeeps all interesting properties such as high spatial adaptivity that are already known for wavelet estimators in the case of Gaussian regression.

It turns out that appropriately tuned versions of this estimator attain the optimal uniform rate of convergence of theirL2risk in a wide variety of Besov smoothness classes, including classes where linear estimators (kernel, spline) are not able to attain this rate. Some simulations indicate the usefulness of the new method in cases of high spatial inhomogeneity.
Androgen and gonadotropin patterns differ in HIV‐1‐infected men who develop lipoatrophy during antiretroviral therapy: a case–control study
Authors: Wunder DM , Fux CA , Bersinger NA , Mueller NJ , Hirschel B , Cavassini M , Elzi L , Schmid P , Bernasconi E , Mueller B , Furrer H ,   the Swiss HIV Cohort Study
Published: May 2008

Objectives

We compared androgen and gonadotropin values in HIV‐infected men who did and did not develop lipoatrophy on combination antiretroviral therapy (cART).

Methods

From a population of 136 treatment‐naïve male Caucasians under successful zidovudine/lamivudine‐based cART, the 10 patients developing lipoatrophy (cases) were compared with 87 randomly chosen controls. Plasma levels of free testosterone (fT), dehydroepiandrosterone (DHEA), follicle‐stimulating hormone and luteinizing hormone (LH) were measured at baseline and after 2 years of cART.

Results

At baseline, 60% of the cases and 71% of the controls showed abnormally low fT values. LH levels were normal or low in 67 and 94% of the patients, respectively, indicating a disturbance of the hypothalamic–pituitary–gonadal axis. fT levels did not significantly change after 2 years of cART. Cases showed a significant increase in LH levels, while controls showed a significant increase in DHEA levels. In a multivariate logistic regression model, lipoatrophy was associated with higher baseline DHEA levels (P=0.04), an increase in LH levels during cART (P=0.001), a lower body mass index and greater age.

Conclusions

Hypogonadism is present in the majority of HIV‐infected patients. The development of cART‐related lipoatrophy is associated with an increase in LH and a lack of increase in DHEA levels.
Modelling University Governance
Authors: Trakman Leon
Published: May 2008

Twentieth century governance models used in public universities are subject to increasing doubt across the English‐speaking world. Governments question if public universities are being efficiently governed; if their boards of trustees are adequately fulfilling their trust obligations towards multiple stakeholders; and if collegial models of governance are working in increasingly complex educational environments. With declining public funding for tertiary education, growing international competition among institutions of higher learning in our information age and worrisome evidence of dysfunctional governance, critics question if established governance structures are able to meet these and other challenges. Some insist that members of faculty are most suited to govern public universities because they appreciate the vision and mission of the university. Others demand that boards of governors be skilled in financial matters and drawn primarily from corporate life. Yet others call for governance based on trust and confidence between those who govern and those who are governed. The article evaluates competing trends in models of university governance in the United Kingdom, the British Commonwealth and the United States. Arguing against a one‐size‐fits‐all model, it sets out specific factors to consider in reforming governance models to meet the demands of our times.
Cerebral Venous Thrombosis After Epidural Blood Patch: Coincidence or Causal Relation? A Case Report and Review of the Literature
Authors: Kueper M , Goericke SL , Kastrup O
Published: May 2008

We report on a female patient who developed post-dural puncture headache (PDPH) after epidural analgesia for delivery. Treatment with epidural blood patch led to complete headache remission and the patient was discharged. Two days later the patient was readmitted with hemihypaesthesia and mild hemiparesis of the right side. Magnetic resonance imaging showed a small left parietal cortical haemorrhage probably following cerebral venous thrombosis (CVT). Coagulation screening detected heterozygous Factor V mutation. Headache is a common symptom of PDPH and CVT. Review of the literature revealed five patients in puerperal state, who developed CVT in close temporal relationship after blood patch treatment for PDPH. Change of headache character with loss of postural influence was reported frequently before diagnosis of CVT was confirmed. These findings may indicate a causal relationship.
Meanings and Experiences of Menstruation: Perceptions of Institutionalized Women with an Intellectual Disability
Authors: Chou Yueh‐Ching , Lu Zxy‐Yann Jane , Wang Frank T. Y. , Lan Chang‐Fu , Lin Li‐Chan
Published: May 2008

Background:  No studies have ever been conducted concerning menstrual experiences among women with an intellectual disability in Taiwan.

Materials and Methods:  An in‐depth interview was conducted at three public institutions and perceptions and experiences regarding menstruation were elicited from 55 women aged 21–65 years.

Results:  The participants knew about menstrual blood and could recognize the experiences of period pain and its link to femininity. The women’s management of menstruation played a big part in their institutionalized life, where they had relatively limited choice and autonomy. Positive feelings towards the menstrual cycle were experienced by some participants; however, many had negative attitudes towards sexual activities or parenting, even though they knew the association between menstruation and pregnancy.

Conclusions:  Although these women’s experiences of perimenstrual symptoms are quite similar to those of women without intellectual disability, their menstrual management, interpretations and attitudes to menses are influenced by their institutional life and by the society at large.
Temperature perception and signal transduction in plants
Authors: Penfield Steven
Published: May 2008

Plants can show remarkable responses to small changes in temperature, yet one of the great unknowns in plant science is how that temperature signal is perceived. The identity of the early components of the temperature signal transduction pathway also remains a mystery. To understand the consequences of anthropogenic environmental change we will have to learn much more about the basic biology of how plants sense temperature. Recent advances show that many known plant‐temperature responses share common signalling components, and suggest ways in which these might be linked to form a plant temperature signalling network.

Contents

Summary
615

I.
Introduction
616

II.
Plant acclimatization to cold
616

III.
The acquisition of thermotolerance
617

IV.
Abscisic acid and extreme temperature responses
617

V.
Altering plant fatty acid composition affects plant tolerance to temperature extremes
617

VI.
Temperature regulates the protein stability of fatty acid desaturases
618

VII.
Membrane fluidity and temperature sensing
618

VIII.
The role of gibberellin in temperature‐regulated growth
619

IX.
Temperature‐regulated GA synthesis controls seed germination
619

X.
Temperature regulates auxin levels to control plant growth rhythms
620

XI.
The circadian clock: temperature entrainment and compensation
620

XII.
Temperature regulates flowering time independently of GA synthesis
621

XIII.
Temperature‐responsive periods control annual growth in trees
621

XIV.
Temperature response pathways share common signalling components
622

XV.
Flowering time regulators with roles in cold acclimatization
623

XVI.
A general role for the circadian clock in temperature signal transduction
623

XVII.
Cytosolic calcium may integrate temperature and circadian signalling
624

XVIII.
The role of phytochrome signalling in temperature responses
624

XIX.
Conclusions
625

References
625
Cell cycle‐driven neuronal apoptosis specifically linked to amyloid peptide Aβ1–42 exposure is not exacerbated in a mouse model of presenilin‐1 familial Alzheimer’s disease
Authors: Malik Bilal , Currais Antonio , Soriano Salvador
Published: May 2008

We have shown previously that β‐catenin and cyclin D1 are up‐regulated in cortical neurons from homozygous mice carrying the familial Alzheimer’s disease (FAD) presenilin‐1 M146V mutation in a knock‐in model (PS1 KIM146V mice), leading to cell cycle‐associated apoptosis. Here, we have aimed to determine (i) whether this phenotype is present in heterozygous PS1 KIM146V mice, which reflects more accurately the PS1 FAD condition in humans and (ii) whether Aβ1–42, which is invariably present in the PS1 FAD brain and is thought to affect neuronal cell cycle kinetics, may contribute to the abnormal cell cycle/cell death phenotype seen in PS1 KIM146V mice. We demonstrate that cell cycle‐linked apoptosis occurs in heterozygous PS1 KIM146V post‐mitotic neurons. In addition, there is a significant Aβ‐associated increase in cell cycle and cell death that is not further modified by the PS1 KIM146V mutation. Our results are consistent with a cell cycle‐associated neurodegeneration model in the PS1 FAD brain in which the loss of PS1‐dependent β‐catenin regulatory function is sufficient to commit susceptible neurons to an abortive cell cycle, and may act synergistically with the Aβ cytotoxic challenge present in the PS1 FAD brain to expand the neuronal population susceptible to cell cycle‐driven apoptosis.
Regulation of neural progenitor cell motility by ceramide and potential implications for mouse brain development
Authors: Wang Guanghu , Krishnamurthy Kannan , Chiang Ying‐Wei , Dasgupta Somsankar , Bieberich Erhard
Published: May 2008

We provide evidence that the sphingolipid ceramide, in addition to its pro‐apoptotic function, regulates neural progenitor (NP) motility in vitro and brain development in vivo. Ceramide (N‐palmitoyl d‐erythro sphingosine and N‐oleoyl d‐erythro sphingosine) and the ceramide analog N‐oleoyl serinol (S18) stimulate migration of NPs in scratch (wounding) migration assays. Sphingolipid depletion by inhibition of de novo ceramide biosynthesis, or ceramide inactivation using an anti‐ceramide antibody, obliterates NP motility, which is restored by ceramide or S18. These results suggest that ceramide is crucial for NP motility. Wounding of the NP monolayer activates neutral sphingomyelinase indicating that ceramide is generated from sphingomyelin. In membrane processes, ceramide is co‐distributed with its binding partner atypical protein kinase C ζ/λ (aPKC), and Cdc42, α/β‐tubulin, and β‐catenin, three proteins involved in aPKC‐dependent regulation of cell polarity and motility. Sphingolipid depletion by myriocin prevents membrane translocation of aPKC and Cdc42, which is restored by ceramide or S18. These results suggest that ceramide‐mediated membrane association of aPKC/Cdc42 is important for NP motility. In vivo, sphingolipid depletion leads to ectopic localization of mitotic or post‐mitotic neural cells in the embryonic brain, while S18 restores the normal brain organization. In summary, our study provides novel evidence that ceramide is critical for NP motility and polarity in vitro and in vivo.
Extracellular levels of brain aspartate, glutamate and GABA during an inhibitory avoidance response in the rat
Authors: Ballini Chiara , Corte Laura Della , Pazzagli Marta , Colivicchi Maria Alessandra , Pepeu Giancarlo , Tipton Keith F. , Giovannini Maria Grazia
Published: May 2008

The extracellular levels of aspartate, glutamate and GABA were measured by microdialysis, coupled with an HPLC method, in rat prefrontal cortex (mPFC) and ventral hippocampus (VH) before and during the performance of a step‐down inhibitory task. The basal levels of glutamate were about 50% higher than those of aspartate, and GABA levels were about 20‐folds smaller than those of the excitatory amino acids. There were no significant differences in the basal levels of any of the three amino acids between the two brain regions. The extracellular levels of aspartate increased during acquisition and recall trials in both VH and mPFC, whereas those of glutamate increased in the VH during acquisition only. A significant increase in GABA levels was also detected during acquisition but only in the mPFC. The neuronal origin of the increased extracellular levels of aspartate, glutamate and GABA was demonstrated by administering tetrodotoxin directly into the mPFC or VH by reverse dialysis. These findings, together with previous evidence from our and other laboratories, indicate a differential release of aspartate and glutamate from excitatory neurons during the performance of behavioral responses, and therefore, distinct roles for the two excitatory amino acids should be envisaged.
Markers of cellular senescence in zero hour biopsies predict outcome in renal transplantation
Authors: Koppelstaetter Christian , Schratzberger Gabriele , Perco Paul , Hofer Johannes , Mark Walter , Öllinger Robert , Oberbauer Rainer , Schwarz Christoph , Mitterbauer Christa , Kainz Alexander , Karkoszka Henryk , Wiecek Andrzej , Mayer Bernd , Mayer Gert
Published: May 2008

Although chronological donor age is the most potent predictor of long‐term outcome after renal transplantation, it does not incorporate individual differences of the aging‐process itself. We therefore hypothesized that an estimate of biological organ age as derived from markers of cellular senescence in zero hour biopsies would be of higher predictive value. Telomere length and mRNA expression levels of the cell cycle inhibitors CDKN2A (p16INK4a) and CDKN1A (p21WAF1) were assessed in pre‐implantation biopsies of 54 patients and the association of these and various other clinical parameters with serum creatinine after 1 year was determined. In a linear regression analysis, CDKN2A turned out to be the best single predictor followed by donor age and telomere length. A multiple linear regression analysis revealed that the combination of CDKN2A values and donor age yielded even higher predictive values for serum creatinine 1 year after transplantation. We conclude that the molecular aging marker CDKN2A in combination with chronological donor age predict renal allograft function after 1 year significantly better than chronological donor age alone.