Parameter estimation for noncausal ARMA models of non-Gaussiansignals via cumulant matching

We consider the problem of estimating the parameters of a stable (stationary), scalar ARMA(p,q) signal model driven by an i.i.d. non-Gaussian sequence. The driving noise sequence is not observed. The signal is allowed to be nonminimum phase and/or noncausal (i.e., poles may lie both inside as well as outside the unit circle). We address the problem of parameter identifiability given the higher order cumulants of the signal on a finite set of lags. The sufficient set of lags required to achieve parameter identifiability is the smallest to date. The sufficient conditions for parameter identifiability are also the least restrictive to date. We also propose a frequency-domain approach for time-domain, nonlinear optimization of a quadratic cumulant matching criterion. Illustrative computer simulation results are presented     

Maximum likelihood estimation of K-distribution parameters via the expectation-maximization algorithm

Maximum likelihood (ML) estimates of K-distribution parameters are derived using the expectation maximization (EM) approach. This approach demonstrates the computational advantages compared with 2-D numerical maximization of the likelihood function using a Nelder-Mead approach. For large datasets, the EM approach yields more accurate estimates than those of a non-ML estimation technique.     

Maximum likelihood estimation of K distribution parameters for SARdata

The K distribution has proven to be a promising and useful model for backscattering statistics in synthetic aperture radar (SAR) imagery. However, most studies to date have relied on a method of moments technique involving second and fourth moments to estimate the parameters of the K distribution. The variance of these parameter estimates is large in cases where the sample size is small and/or the true distribution of backscattered amplitude is highly non-Rayleigh. The present authors apply a maximum likelihood estimation method directly to the K distribution. They consider the situation for single-look SAR data as well as a simplified model for multilook data. They investigate the accuracy and uncertainties in maximum likelihood parameter estimates as functions of sample size and the parameters themselves. They also compare their results with those from a new method given by Raghavan (1991) and from a nonstandard method of moments technique; maximum likelihood parameter estimates prove to be at least as accurate as those from the other estimators in all cases tested, and are more accurate in most cases. Finally, they compare the simplified multilook model with nominally four-look SAR data acquired by the Jet Propulsion Laboratory AIRSAR over sea ice in the Beaufort Sea during March 1988. They find that the model fits data from both first-year and multiyear ice well and that backscattering statistics from each ice type are moderately non-Rayleigh. They note that the distributions for the data set differ too little between ice types to allow discrimination based on differing distribution parameters     

Maximum likelihood estimation of Weibull parameters for twoindependent competing risk

A stable technique for obtaining the maximum-likelihood estimate of Weibull parameters of the life distributions of two components that form a series system is presented. The technique requires much more computation than the procedure of H. Kanie and Y. Nonaka (1985). Simulation results, however, show that the standard deviation of the estimated values of the Weibull parameters is greatly reduced. This technique does not require the concomitant indicator and can be applied not only for complete data but for randomly censored data     

Maximum likelihood estimation of exponential distribution parameters using interval data

This paper addresses the problem of estimating, by the method of maximum likelihood (ML), the location parameter (when present) and scale parameter of the exponential distribution (ED) from interval data. Interval data are defined as two data values that surround an unknown failure observation. Such observations occur naturally, during periodic inspections, for example, when only the time interval during which the failure occurred is known. The appropriate (conditional) log-likelihood functions are derived, as are expressions for the asymptotic variances and covariances of the ML parameter estimates. To illustrate the calculations involved, two numerical examples are discussed.     

Two-dimensional spectrum estimation using noncausal autoregressive models

Two-dimensional (2-D) spectrum estimation from raw data is of interest in signal and image processing. A parametric technique for spectrum estimation using 2-D noncausal autoregressive (NCAR) models is given. The NCAR models characterize the statistical dependency of the observation at location s on its neighbors in all directions. This modeling assumption reduces the spectrum estimation problem to two subproblems: the choice of appropriate structure of the NCAR model and the estimation of parameters in NCAR models. By assuming that the true structure of the NCAR model is known, we first analyze the existence and uniqueness of Gaussian maximum likelihood (GML) estimates of NCAR model parameters. Due to the noncausal nature of the models, the computation of GML estimates is burdensome. By assuming specific boundary conditions, computationally tractable expressions are obtained for the likelihood function. Expressions for the asymptotic covariance matrix of the GML estimates as well as the simultaneous confidence bands for the estimated spectrum using GML estimates are derived. Finally, the usefulness of the method is illustrated by computer simulation results.     

Maximum likelihood estimation of the parameters of discretefractionally differenced Gaussian noise process

A maximum-likelihood estimation procedure is constructed for estimating the parameters of discrete fractionally differenced Gaussian noise from an observation set of finite size N. The procedure does not involve the computation of any matrix inverse or determinant. It requires N²/2+O(N) operations. The expected value of the loglikelihood function for estimating the parameter d of fractionally differenced Gaussian noise (which corresponds to a parameter of the equivalent continuous-time fractional Brownian motion related to its fractal dimension) is shown to have a unique maximum that occurs at the true value of d. A Cramer-Rao bound on the variance of any unbiased estimate of d obtained from a finite-sized observation set is derived. It is shown experimentally that the maximum-likelihood estimate of d is unbiased and efficient when finite-size data sets are used in the estimation procedure. The proposed procedure is extended to deal with noisy observations of discrete fractionally differenced Gaussian noise     

Adaptive identification of nonminimum phase ARMA models usinghigher order cumulants alone

An adaptive identification algorithm for causal nonminimum phase ARMA models in additive colored Gaussian noise is proposed. The algorithm utilizes higher order cumulants of the observed signal alone. It estimates the AR and MA parameters successively in each iteration without computing the residual time series. The steepest descent method is used for parameter updating     

Maximum likelihood estimation of the Burr XII parameters with censored and uncensored data

This paper presents the methodology for obtaining point and interval estimates of the parameters of the Burr Type XII distribution with multiple-censored and singly-censored data (Type I censoring or Type II censoring) as well as complete data, using the maximum likelihood method. The basis is the likelihood expression for multiple-censored data. The other types of censoring and complete data are treated as special cases. A FORTRAN language program was used. Some illustrative examples are included.     

Maximum likelihood estimation of parameters of several continuous and discrete failure distributions

A comprehensive quantitative treatment is presented for maximum-likelihood estimation of parameters of the following continuous and discrete failure distributions: (1) Exponential, (2) Gamma, (3) Weibull, (4) Normal, (5) Lognormal, (6) Extreme value, (7) Poisson, (8) Binomial and (9) Geometric.     

Maximum likelihood estimation of Burr XII distribution parameters under Type II censoring

The mathematical theory for the point estimation of the parameters of the Burr Type XII distribution by maximum likelihood (ML) is developed for Type II censored samples. Also derived are necessary and sufficient conditions on the sample data that guarantee the existence, uniqueness and finiteness of the ML parameter estimates for all possible permissible parameter combinations. The asymptotic theory of ML is invoked to obtain approximate confidence intervals for the ML parameter estimates. An application to reliability data arising in a life test experiment is discussed.     

Maximum likelihood estimation of parameters in multivariate Gaussian stochastic processes (Corresp.)

We outline a proof of the strong consistency of the maximum likelihood estimate of the parameters of Gaussian random processes possessing linear autoregressive moving average or state space representations.     

A recursive maximum likelihood algorithm for ARMA spectral estimation

A spectral estimation technique is presented for autoregressive moving-average (ARMA) processes. The technique is based on a parameter estimation technique known as the rec ursive maximum likelihood (RML) method. The recursive spectral estimation algorithm is presented and its asymptotic properties are discussed. Simulation results are presented to illustrate the performance of the estimator for various types of data.     

Maximum likelihood sequence estimation from the lattice viewpoint

Considers the problem of data detection in multilevel lattice-type modulation systems in the presence of intersymbol interference and additive white Gaussian noise. The conventional maximum likelihood sequence estimator using the Viterbi algorithm has a time complexity of O(m^ν+1) operations per symbol and a space complexity of O(δm^ν) storage elements, where m is the size of input alphabet, ν is the length of channel memory, and δ is the truncation depth. By revising the truncation scheme and viewing the channel as a linear transform, the authors identify the problem of maximum likelihood sequence estimation with that of finding the nearest lattice point. From this lattice viewpoint, the lattice sequence estimator for PAM systems is developed, which has the following desired properties: 1) its expected time-complexity grows as δ² as SNR→∞; 2) its space complexity grow as δ; and 3) its error performance is effectively optimal for sufficiently large m. A tight upper bound on the symbol error probability of the new estimator is derived, and is confirmed by the simulation results of an example channel. It turns out that the estimator is effectively optimal for m⩾4 and the loss in signal-to-noise ratio is less than 0.5 dB even for m=2. Finally, limitations of the proposed estimator are also discussed     

Maximum likelihood filters in spectral estimation problems

This paper begins with a classification of power spectral estimates from the point of view of bank filter analysis. To reinforce the interest of such a classification, a review of the main and most familiar procedures for spectral estimation is included. Starting from the most general approach, due to Frost, we indicate why it is not appropriate to classify Capon's maximum likelihood method as a low resolution procedure.The second part of the paper deals with a modification of the so-called maximum likelihood estimate in order to obtain the resolution which corresponds to a power density estimate. The modification provided here consists in a bandwidth normalization. The resulting estimate shows how the area of application of ML filters (as the data depending filters reported some years ago by Capon and Lacoss could be named) is considerably extended to a reliable procedure for power level and power density level estimation.We also explain in this paper how to get cross-spectral estimates from ML filters. From our point of view, this approach is the only one, among currently reported methods, that enhances the adequate levels of quality in order to compete with classical Fourier analyzers.In addition, the interesting ideas of Pisarenko about power function estimates can also be applied to the new approach presented here. The resulting family of power function estimates can further improve resolution up to the quality provided by SVD like methods, but avoiding the computational burden associated with them.     

Maximum likelihood trend estimation in exponential noise

This paper considers the problem of estimating a linear trend in noise, where the noise is modeled as independent and identically distributed (i.i.d.) random process with exponential distribution. The corresponding maximum likelihood parameter estimator of the trend and noise parameters is derived, and its performance is analyzed. It turns out that the resulting maximum likelihood estimator has to solve a linear programming problem with number of constraints equal to the number of received data. A recursive form of the maximum likelihood estimator, which makes it suitable for implementation in real-time systems, is then proposed. The memory requirements of the recursive algorithm are data dependent and are investigated by simulations using both computer-generated and recorded data sets     

Maximum likelihood signal estimation for polarization sensitivearrays

The authors consider the problem of separating and estimating the waveforms of superimposed signals received by a polarization-sensitive array. Signal estimation is accomplished by a two-step maximum likelihood procedure: (i) The directions of arrival and polarization parameters of all the signals are estimated. (ii) The estimated signal is obtained as a linear combination of the array outputs, with weights which are computed from the estimated direction/polarization parameters. The objective of this study is to analyze the quality of the estimated signal in terms of the output signal-to-interference ratio (SIR) and output signal-to-noise ratio (SNR). Closed-form expressions are derived for the output SIR and SNR of a general diversely polarized array. By evaluating these expressions for selected test cases it is shown that polarization-sensitive arrays can provide significantly higher output SIR and SNR than uniformly polarized arrays. The performance improvement is especially significant for closed spaced sources with sufficiently different polarization characteristics     

Maximum pseudo likelihood estimation in network tomography

Network monitoring and diagnosis are key to improving network performance. The difficulties of performance monitoring lie in today's fast growing Internet, accompanied by increasingly heterogeneous and unregulated structures. Moreover, these tasks become even harder since one cannot rely on the collaboration of individual routers and servers to measure network traffic directly. Even though the aggregative nature of possible network measurements gives rise to inverse problems, existing methods for solving inverse problems are usually computationally intractable or statistically inefficient. A pseudo likelihood approach is proposed to solve a group of network tomography problems. The basic idea of pseudo likelihood is to form simple subproblems and ignore the dependences among the subproblems to form a product likelihood of the subproblems. As a result, this approach keeps a good balance between the computational complexity and the statistical efficiency of the parameter estimation. Some statistical properties of the pseudo likelihood estimator, such as consistency and asymptotic normality, are established. A pseudo expectation-maximization (EM) algorithm is developed to maximize the pseudo log-likelihood function. Two examples, with simulated or real data, are used to illustrate the pseudo likelihood proposal: 1) inference of the internal link delay distributions through multicast end-to-end measurements; 2) origin-destination matrix estimation through link traffic counts.     

Maximum likelihood estimation for InSAR image co-registration

This paper presents a closed-form robust phase correlation based algorithm for performing image registration to subpixel accuracy. The subpixel translational shift information is directly ob- tained from the phase of the normalized cross power spectrum by using Maximum Likelihood Esti- mation （MLE）. The proposed algorithm also has slighter time complexity. Experimental results show that the proposed algorithm yields superior registration precision on the Cram@r-Rao Bound （CRB） in the presence of aliasing and noise.     

Maximum likelihood estimation for a multivariate autoregressivemodel

The paper provides an analytical expression for the exact log likelihood function and its first derivatives for a multivariate autoregressive model. Based on these results, two algorithms for constructing the maximum likelihood estimate, using the Fisher's scoring technique, are proposed. The estimated model is guaranteed to be stable. Simulation examples show that this algorithm has good convergence properties and the resulting maximum likelihood estimator could perform better than earlier methods, in cases where the record length is short and the autoregressive polynomial has roots near the unit circle