Bayes quadratic unbiased estimator of spatial covariance parameters

This work presents Bayes invariant quadratic unbiased estimator, for short BAIQUE. Bayesian approach is used here to estimate the covariance functions of the regionalized variables which appear in the spatial covariance structure in mixed linear model. Firstly a brief review of spatial process, variance covariance components structure and Bayesian inference is given, since this project deals with these concepts. Then the linear equations model corresponding to BAIQUE in the general case is formulated. That Bayes estimator of variance components with too many unknown parameters is complicated to be solved analytically. Hence, in order to facilitate the handling with this system, BAIQUE of spatial covariance model with two parameters is considered. Bayesian estimation arises as a solution of a linear equations system which requires the linearity of the covariance functions in the parameters. Here the availability of prior information on the parameters is assumed. This information includes apriori distribution functions which enable to find the first and the second moments matrix. The Bayesian estimation suggested here depends only on the second moment of the prior distribution. The estimation appears as a quadratic form y'Ay , where y is the vector of filtered data observations. This quadratic estimator is used to estimate the linear function of unknown variance components. The matrix A of BAIQUE plays an important role. If such a symmetrical matrix exists, then Bayes risk becomes minimal and the unbiasedness conditions are fulfilled. Therefore, the symmetry of this matrix is elaborated in this work. Through dealing with the infinite series of matrices, a representation of the matrix A is obtained which shows the symmetry of A. In this context, the largest singular value of the decomposed matrix of the infinite series is considered to deal with the convergence condition and also it is connected with Gerschgorin Discs and Poincare theorem. Then the BAIQUE model for some experimental designs is computed and compared. The comparison deals with different aspects, such as the influence of the position of the design points in a fixed interval. The designs that are considered are those with their points distributed in the interval [0, 1]. These experimental structures are compared with respect to the Bayes risk and norms of the matrices corresponding to distances, covariance structures and matrices which have to satisfy the convergence condition. Also different types of the regression functions and distance measurements are handled. The influence of scaling on the design points is studied, moreover, the influence of the covariance structure on the best design is investigated and different covariance structures are considered. Finally, BAIQUE is applied for real data. The corresponding outcomes are compared with the results of other methods for the same data. Thereby, the special BAIQUE, which estimates the general variance of the data, achieves a very close result to the classical empirical variance.

Une famille de distributions symétriques et leptocurtiques représentée par la différence de deux variables aléatoires gamma

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

How Does the New Keynesian Monetary Model Fit in the U.S. and the Eurozone? an Indirect Inference Approach

This paper estimates a standard version of the New Keynesian monetary (NKM) model under alternative specifications of the monetary policy rule using U.S. and Eurozone data. The estimation procedure implemented is a classical method based on the indirect inference principle. An unrestricted VAR is considered as the auxiliary model. On the one hand, the estimation method proposed overcomes some of the shortcomings of using a structural VAR as the auxiliary model in order to identify the impulse response that defines the minimum distance estimator implemented in the literature. On the other hand, by following a classical approach we can further assess the estimation results found in recent papers that follow a maximum-likelihood Bayesian approach. The estimation results show that some structural parameter estimates are quite sensitive to the specification of monetary policy. Moreover, the estimation results in the U.S. show that the fit of the NKM under an optimal monetary plan is much worse than the fit of the NKM model assuming a forward-looking Taylor rule. In contrast to the U.S. case, in the Eurozone the best fit is obtained assuming a backward-looking Taylor rule, but the improvement is rather small with respect to assuming either a forward-looking Taylor rule or an optimal plan.

Statistical problems related to excitation threshold and reset value of membrane potentials

Die vorliegende Arbeit ist motiviert durch biologische Fragestellungen bezüglich des Verhaltens von Membranpotentialen in Neuronen. Ein vielfach betrachtetes Modell für spikende Neuronen ist das Folgende. Zwischen den Spikes verhält sich das Membranpotential wie ein Diffusionsprozess X der durch die SDGL dX_t= beta(X_t) dt+ sigma(X_t) dB_t gegeben ist, wobei (B_t) eine Standard-Brown'sche Bewegung bezeichnet. Spikes erklärt man wie folgt. Sobald das Potential X eine gewisse Exzitationsschwelle S überschreitet entsteht ein Spike. Danach wird das Potential wieder auf einen bestimmten Wert x_0 zurückgesetzt. In Anwendungen ist es manchmal möglich, einen Diffusionsprozess X zwischen den Spikes zu beobachten und die Koeffizienten der SDGL beta() und sigma() zu schätzen. Dennoch ist es nötig, die Schwellen x_0 und S zu bestimmen um das Modell festzulegen. Eine Möglichkeit, dieses Problem anzugehen, ist x_0 und S als Parameter eines statistischen Modells aufzufassen und diese zu schätzen. In der vorliegenden Arbeit werden vier verschiedene Fälle diskutiert, in denen wir jeweils annehmen, dass das Membranpotential X zwischen den Spikes eine Brown'sche Bewegung mit Drift, eine geometrische Brown'sche Bewegung, ein Ornstein-Uhlenbeck Prozess oder ein Cox-Ingersoll-Ross Prozess ist. Darüber hinaus beobachten wir die Zeiten zwischen aufeinander folgenden Spikes, die wir als iid Treffzeiten der Schwelle S von X gestartet in x_0 auffassen. Die ersten beiden Fälle ähneln sich sehr und man kann jeweils den Maximum-Likelihood-Schätzer explizit angeben. Darüber hinaus wird, unter Verwendung der LAN-Theorie, die Optimalität dieser Schätzer gezeigt. In den Fällen OU- und CIR-Prozess wählen wir eine Minimum-Distanz-Methode, die auf dem Vergleich von empirischer und wahrer Laplace-Transformation bezüglich einer Hilbertraumnorm beruht. Wir werden beweisen, dass alle Schätzer stark konsistent und asymptotisch normalverteilt sind. Im letzten Kapitel werden wir die Effizienz der Minimum-Distanz-Schätzer anhand simulierter Daten überprüfen. Ferner, werden Anwendungen auf reale Datensätze und deren Resultate ausführlich diskutiert.

Simulations for Gravitational Lensing of 21 cm Radiation at EoR and Post-EoR Redshifts

21 cm cosmology opens an observational window to previously unexplored cosmological epochs such as the Epoch of Reionization (EoR), the Cosmic Dawn and the Dark Ages using powerful radio interferometers such as the planned Square Kilometer Array (SKA). Among all the other applications which can potentially improve the understanding of standard cosmology, we study the promising opportunity given by measuring the weak gravitational lensing sourced by 21 cm radiation. We performed this study in two different cosmological epochs, at a typical EoR redshift and successively at a post-EoR redshift. We will show how the lensing signal can be reconstructed using a three dimensional optimal quadratic lensing estimator in Fourier space, using single frequency band or combining multiple frequency band measurements. To this purpose, we implemented a simulation pipeline capable of dealing with issues that can not be treated analytically. Considering the current SKA plans, we studied the performance of the quadratic estimator at typical EoR redshifts, for different survey strategies and comparing two thermal noise models for the SKA-Low array. The simulation we performed takes into account the beam of the telescope and the discreteness of visibility measurements. We found that an SKA-Low interferometer should obtain high-fidelity images of the underlying mass distribution in its phase 1 only if several bands are stacked together, covering a redshift range that goes from z=7 to z=11.5. The SKA-Low phase 2, modeled in order to improve the sensitivity of the instrument by almost an order of magnitude, should be capable of providing images with good quality even when the signal is detected within a single frequency band. Considering also the serious effect that foregrounds could have on this detections, we discussed the limits of these results and also the possibility provided by these models of measuring an accurate lensing power spectrum.

A Reliable Residual Based A Posteriori Error Estimator for a Quadratic Finite Element Method for the Elliptic Obstacle Problem

A residual based a posteriori error estimator is derived for a quadratic finite element method (FEM) for the elliptic obstacle problem. The error estimator involves various residuals consisting of the data of the problem, discrete solution and a Lagrange multiplier related to the obstacle constraint. The choice of the discrete Lagrange multiplier yields an error estimator that is comparable with the error estimator in the case of linear FEM. Further, an a priori error estimate is derived to show that the discrete Lagrange multiplier converges at the same rate as that of the discrete solution of the obstacle problem. The numerical experiments of adaptive FEM show optimal order convergence. This demonstrates that the quadratic FEM for obstacle problem exhibits optimal performance.

The effect of halogen atom on the molecular quadratic nonlinearity of half sandwich complexes in the presence of an acceptor

Half sandwich complexes of the type [CpM(CO)(n)X] {X=Cl, Br, I; If, M=Fe, Ru; n=2 and if M=Mo; n=3} and [CpNiPPh3X] {X=Cl, Br, I} have been synthesized and their second order molecular nonlinearity (beta) measured at 1064 nm in CHCl3 by the hyper-Rayleigh scattering technique. Iron complexes consistently display larger beta values than ruthenium complexes while nickel complexes have marginally larger beta values than iron complexes. In the presence of an acceptor ligand such as CO or PPh3, the role of the halogen atom is that of a pi donor. The better overlap of Cl orbitals with Fe and Ni metal centres make Cl a better pi donor than Br or I in the respective complexes. Consequently, M-pi interaction is stronger in Fe/Ni-Cl complexes. The value of beta decreases as one goes down the halogen group. For the complexes of 4d metal ions where the metal-ligand distance is larger, the influence of pi orbital overlap appears to be less important, resulting in moderate changes in beta as a function of halogen substitution. (C) 2006 Elsevier B.V. All rights reserved.

Quadratic M-Estimators for ARCH-Type Processes

This paper addresses the issue of estimating semiparametric time series models specified by their conditional mean and conditional variance. We stress the importance of using joint restrictions on the mean and variance. This leads us to take into account the covariance between the mean and the variance and the variance of the variance, that is, the skewness and kurtosis. We establish the direct links between the usual parametric estimation methods, namely, the QMLE, the GMM and the M-estimation. The ususal univariate QMLE is, under non-normality, less efficient than the optimal GMM estimator. However, the bivariate QMLE based on the dependent variable and its square is as efficient as the optimal GMM one. A Monte Carlo analysis confirms the relevance of our approach, in particular, the importance of skewness.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Quadratic effective action for QED in D=2,3 dimensions

We calculate the effective action for quantum electrodynamics (QED) in D=2,3 dimensions at the quadratic approximation in the gauge fields. We analyze the analytic structure of the corresponding nonlocal boson propagators nonperturbatively in k/m. In two dimensions for any nonzero fermion mass, we end up with one massless pole for the gauge boson. We also calculate in D=2 the effective potential between two static charges separated by a distance L and find it to be a linearly increasing function of L in agreement with the bosonized theory (massive sine-Gordon model). In three dimensions we find nonperturbatively in k/m one massive pole in the effective bosonic action leading to screening. Fitting the numerical results we derive a simple expression for the functional dependence of the boson mass upon the dimensionless parameter e2/m. ©2000 The American Physical Society.

Lattice constellations and codes from quadratic number fields

We propose new classes of linear codes over integer rings of quadratic extensions of Q, the field of rational numbers. The codes are considered with respect to a Mannheim metric, which is a Manhattan metric modulo a two-dimensional (2-D) grid. In particular, codes over Gaussian integers and Eisenstein-Jacobi integers are extensively studied. Decoding algorithms are proposed for these codes when up to two coordinates of a transmitted code vector are affected by errors of arbitrary Mannheim weight. Moreover, we show that the proposed codes are maximum-distance separable (MDS), with respect to the Hamming distance. The practical interest in such Mannheim-metric codes is their use in coded modulation schemes based on quadrature amplitude modulation (QAM)-type constellations, for which neither the Hamming nor the Lee metric is appropriate.

Short distance QCD contribution to the electroweak mass difference of pions

It is known that the short distance QCD contribution to the mass difference of pions is quadratic on the quark masses, and irrelevant with respect to the long distance part. It is also considered in the literature that its calculation contains infinities, which should be absorbed by the quark mass renormalization. Following a prescription by Craigie, Narison, and Riazuddin of a renormalization-group-improved perturbation theory to deal with the electromagnetic mass shift problem in QCD, we show that the short distance QCD contribution to the electroweak pion mass difference (with mu=md≠0) is finite and, of course, its value is negligible compared to other contributions.

Suitability of distance metrics as indexes of home-range size in tropical rodent species

Estimators of home-range size require a large number of observations for estimation and sparse data typical of tropical studies often prohibit the use of such estimators. An alternative may be use of distance metrics as indexes of home range. However, tests of correlation between distance metrics and home-range estimators only exist for North American rodents. We evaluated the suitability of 3 distance metrics (mean distance between successive captures [SD], observed range length [ORL], and mean distance between all capture points [AD]) as indexes for home range for 2 Brazilian Atlantic forest rodents, Akodon montensis (montane grass mouse) and Delomys sublineatus (pallid Atlantic forest rat). Further, we investigated the robustness of distance metrics to low numbers of individuals and captures per individual. We observed a strong correlation between distance metrics and the home-range estimator. None of the metrics was influenced by the number of individuals. ORL presented a strong dependence on the number of captures per individual. Accuracy of SD and AD was not dependent on number of captures per individual, but precision of both metrics was low with numbers of captures below 10. We recommend the use of SD and AD instead of ORL and use of caution in interpretation of results based on trapping data with low captures per individual.

An optimal estimator for the correlation of CMB anisotropies with Large Scale Structures and its application to WMAP-7year and NVSS

In the thesis we present the implementation of the quadratic maximum likelihood (QML) method, ideal to estimate the angular power spectrum of the cross-correlation between cosmic microwave background (CMB) and large scale structure (LSS) maps as well as their individual auto-spectra. Such a tool is an optimal method (unbiased and with minimum variance) in pixel space and goes beyond all the previous harmonic analysis present in the literature. We describe the implementation of the QML method in the {\it BolISW} code and demonstrate its accuracy on simulated maps throughout a Monte Carlo. We apply this optimal estimator to WMAP 7-year and NRAO VLA Sky Survey (NVSS) data and explore the robustness of the angular power spectrum estimates obtained by the QML method. Taking into account the shot noise and one of the systematics (declination correction) in NVSS, we can safely use most of the information contained in this survey. On the contrary we neglect the noise in temperature since WMAP is already cosmic variance dominated on the large scales. Because of a discrepancy in the galaxy auto spectrum between the estimates and the theoretical model, we use two different galaxy distributions: the first one with a constant bias $b$ and the second one with a redshift dependent bias $b(z)$. Finally, we make use of the angular power spectrum estimates obtained by the QML method to derive constraints on the dark energy critical density in a flat $\Lambda$CDM model by different likelihood prescriptions. When using just the cross-correlation between WMAP7 and NVSS maps with 1.8° resolution, we show that $\Omega_\Lambda$ is about the 70\% of the total energy density, disfavouring an Einstein-de Sitter Universe at more than 2 $\sigma$ CL (confidence level).