High precision technique for PPS estimation in impulsive noise environment

Quasi Maximum Likelihood Estimation of Value at Risk and Expected Shortfall

In this paper, an idea from the quasi-maximum-likelihood (QML) algorithm has been applied to estimation of the direction of arrival (DOA) and parameters of the polynomial phase signals (PPS) impinging on the uniform linear array. The algorithm uses a search over a set of possible DOA values. For considered value, the DOA is compensated and resulted signal parameters are estimated using the original QML approach. In this way, several sets of potential estimates are obtained. Optimal DOA and PPS parameters belong to set that maximizes the proposed cost function. In the proposed approach, parameters are estimated using 2-D search over DOA and window widths used for calculation of the short Fourier transform in the QML that is an important advantage with respect to existing strategies like, for example, the polynomial beamformer requiring multi-dimensional or metaheuristic search strategies. Numerical validation of the proposed approach for both mono- and multi-component signals is provided in the paper. Both far- and near-field scenarios are considered.

This thesis introduces some topics about initial downlink synchronization, algorithm derivations, and program simulations of IEEE 802.16m system. When a mobile station entering to the network, it needs to perform initial synchronization, including of symbol timing offset, carrier frequency offset and preamble index. We utilize the trait which the power of preamble is larger than it of the common data symbol to compute the moving power sum, and then estimate the left boundary of preamble by finding out the peak value of moving power sum. A symbol period from this estimated boundary is regarded as the estimated preamble, which has a phase noise with the exact preamble. We derive the quasi maximum likelihood estimation from the likelihood function of the estimated preamble to obtain fractional carrier frequency offset (FCFO) and the formula of channel estimation. After compensating the estimated fractional carrier frequency offset to the formula of channel estimation, we substitute several reasonable integral carrier frequency offsets (ICFOs) and primary advanced preambles (PA-Preambles) into this formula and obtain channel impulse responses (CIRs). After that, we compute different fine timing offset index 64-points power sum of these CIRs and find out the peak value whose ICFO, PA-Preamble index, and fine timing offset index are regarded as the result of the joint estimation. In terms of simulations, above all, we present a reasonable received signal model of IEEE 802.16m, and then simulate our algorithm in floating point under AWGN (additive white Gaussian noise) channel to verify its performance. Moreover, we test this algorithm under different multipath fading channels, Pedestrian B and SUI-5, at different speed which cause Doppler shifts, and signal to noise ratio (SNR) to observe the effect on this algorithm.

In this study, we introduce a novel approach, called the quasi maximum likelihood estimation (Q-MLE), for estimating large-dimensional matrix factor models. In contrast to the principal component analysis based approach, Q-MLE considers the heteroscedasticity of the idiosyncratic error term, the heteroscedasticity of which is simultaneously estimated with other parameters. Interestingly, under the homoscedasticity assumption of the idiosyncratic error, the Q-MLE estimator encompassed the projected estimator (PE) as a special case. We provide the convergence rates and asymptotic distributions of the Q-MLE estimators under mild conditions. Extensive numerical experiments demonstrate that the Q-MLE method performs better, especially when heteroscedasticity exists. Furthermore, two real examples in finance and macroeconomics reveal factor patterns across rows and columns, which coincide with financial, economic, or geographical interpretations.

On the choice of test for a unit root when the errors are conditionally heteroskedastic

This paper performs a Monte Carlo study on efficient method of moments (EMM), generalized method of moments (GMM), quasi-maximum likelihood estimation (QMLE) and maximum likelihood estimation (MLE) for a continuous-time square-root model under two challenging scenarios – high persistence in mean and strong conditional volatility – that are commonly found in estimating the interest rate process. MLE turns out to be the most efficient of the four methods, but its finite sample inference and convergence rate suffer severely from approximating the likelihood function, especially in the scenario of highly persistent mean. QMLE comes second in terms of estimation efficiency, but it is the most reliable in generating inferences. GMM with lag-augmented moments has, overall, the lowest estimation efficiency, possibly due to the ad hoc choice of moment conditions. EMM shows an accelerated convergence rate in the high-volatility scenario, while its over-rejection bias in the mean persistence scenario is unacceptably large. Finally, under a stylized alternative model of the US interest rates, the overidentification test of EMM obtains the ultimate power for detecting misspecification, while the GMM J-test is increasingly biased downwards in finite samples.

In a multichannel synthetic aperture radar (SAR) system, because of the target uncooperative motion, a ground moving target (GMT) is usually smeared, distorted, and shifted in an SAR image. In this article, a novel approach for multichannel SAR-GMT indication (GMTI) processing is proposed. The main innovations of this method are that a GMT can be well refocused and relocated since the target high-order Doppler parameters can be precisely estimated based on a high-order polynomial phase signal (PPS) model, and the target statistical amplitude and phase information is jointly applied to improve the radial velocity estimation robustness. Compared with the current SAR-GMTI algorithms, the improvements of this method over the existing methods are: 1) the topography interferometric phase can be effectively compensated by applying an adaptive 2-D spectrum filtering technique via iterative processing; 2) a GMT can be well imaged since the target Doppler chirp rate and the quadratic chirp rate can be well estimated via the 2-D coherent integration in the time–frequency plane; and 3) a GMT can be precisely relocated into its original position by applying the generalized amplitude and phase weighting technique. Real-measured SAR data processing results are presented to validate the effectiveness and feasibility of the proposed method.

Simulation evaluation of emerging estimation techniques for multinomial probit models

In this paper, we propose a modified time-domain channel estimation algorithm for OFDM systems working under impulsive noise conditions. The proposed method employs two time-domain windows to reduce additive white Gaussian noise and impulsive noise components included in the estimated channel impulse response (CIR). Meanwhile, the impulse noise equalizer further eliminates the impulse noise. As a result, the modified algorithm can overcome the mean-square error (MSE) floor caused by the conventional method in impulsive noise environment. Simulation results demonstrate that the proposed algorithm can significantly improve the MSE performance compared with the conventional estimation method, and provides about 3dB improvement in SNR for the same BER values in DVB system.

In this paper, we are interested in the problem of parameter estimation of 3-order polynomial phase signals (PPS) affected by additive Gaussian white noise. A new algorithm for 3-order PPS parameter estimation is proposed. Filter bank, short time Radon-Wigner transform, cure fitting and local scope searching algorithm is adopt together to get the parameter's accurate estimation. The merit of this new algorithm is validated by illustrative examples in low (SNR) condition.

SummaryBlundell, Pistaferri, and Preston (American Economic Review, 2008, 98(5), 1887–1921) report an estimate of household consumption insurance with respect to permanent income shocks of 36%. In replicating findings for their model and data, we find that this estimate is distorted by a code error and is not robust to weighting scheme for generalized method of moments (GMM) or consideration of quasi maximum likelihood estimation (QMLE), which produces a significantly higher estimate of consumption insurance at 55%. For sub‐groups by age and education, the differences between estimates across methods are even more pronounced, and QMLE provides new insights into heterogeneity across households compared to the original study. Monte Carlo experiments using non‐normal shocks suggest that consumption insurance estimates for the model are more accurate for QMLE than GMM, including when correcting for bias and especially given a smaller sample such as is only available when looking at sub‐groups.

The aim of this work is the parameter estimation of polynomial-phase signals (PPSs) embedded in white noise. The proposed estimation algorithm is a generalization of the method based on the polynomial-phase transform (PPT), able to solve an ambiguity problem appearing when applying the PPT to PPSs having the same highest order phase coefficients. The proposed approach is based on the intersection of two (or more) signal subspaces sharing only the useful components but not the undesired spurious harmonics. Different signal subspaces are obtained by exploiting a multilag definition of the PPT.

Radix-3 fast algorithms for polynomial time frequency transforms

We consider a model for a multivariate time series where the conditional covariance matrix is a function of a finite-dimensional parameter and the innovation distribution is nonparametric. The semiparametric lower bound for the estimation of the euclidean parameter is characterized, and it is shown that adaptive estimation without reparametrization is not possible. Based on a consistent first-stage estimator (such as quasi maximum likelihood), we propose a semiparametric estimator that estimates the efficient influence function using kernel estimators. We state conditions under which the estimator attains the semiparametric lower bound. For particular models such as the constant conditional correlation model, adaptive estimation of the dynamic part of the model is shown to be possible. To avoid the curse of dimensionality one can, e.g., restrict the multivariate density to the class of spherical distributions, for which we also derive the semiparametric efficiency bound and an estimator that attains this bound. A simulation experiment demonstrates the efficiency gain of the proposed estimator compared with quasi maximum likelihood estimation.Rombouts work was supported by the Centre for Research on e-Finance, HEC Montreal. Hafner gratefully acknowledges financial support by the Fonds Sp ciaux de Recherche (FSR 05) of the Universit catholique de Louvain. The authors thank three anonymous referees for valuable comments and suggestions and Luc Bauwens, Geert Dhaene, Feico Drost, Wolfgang H rdle, Douglas Hodgson, Jens Peter Kreiss, Oliver Linton, and Bas Werker for helpful discussions. We also thank participants of the CORE econometrics seminar, the York annual meeting in econometrics, the annual econometric study group meeting 2002 in Bristol, the 2003 workshop The Art of Semiparametrics in Berlin, and the statistics seminar of the Stockholm School of Economics for valuable comments.

Laser pointing systems for small targets are mainly confronted with two pointing errors, jitter and boresight, arising due to atmospheric turbulence, mechanical vibrations and errors in optical alignment. Knowledge of these parameters provides information about the quality of the pointing and tracking system. In the past, several techniques have been investigated to estimate these parameters from returned laser signals. These include the key ratio technique, the chi-squared method and the maximum likelihood (ML) estimation technique. These techniques have been studied in the literature for Gaussian irradiance profiles. In particular, the ML estimation technique has been found to provide excellent results. In this paper, we extend the ML estimation technique from Gaussian profiles to near-Gaussian irradiance profiles. Our results show that the modified estimator performs much better than a direct application of the original ML estimator.