The problem of estimating the parameter θ, when it is restricted to an interval of the form [ ,1] m , in a class of discrete distributions, including Binomial ( , ), k θ Negative Binomial ( , ), r θ discrete Weibull ( ) θ and etc., is considered. We give necessary and sufficient conditions for which the Bayes estimator of , θ with respect to a two points boundary supported prior is minimax unde...

This paper considers M-estimation of a nonlinear regression model with multiple changepoints occuring at the unknown times. The multi-phase random design regression model, discontinuous in each change-point, have an arbitrary error ε. In the case when the number of jumps is known, the M-estimator of the locations of the breaks and of regression parameters are studied. These estimators are consi...

Consider the regression model Y = m(X)+σ(X)ε, where m = E[Y |X] and σ(X) = V ar[Y |X] are unknown smooth functions and the error ε (with unknown distribution) is independent of X. The pair (X,Y ) is subject to parametric selection bias and the response to right censoring. We construct a new estimator for the cumulative distribution function of the error ε, and develop a bootstrap technique to s...

In diverse engineering problems including wireless communications, the estimate of the signal-to-noise ratio (SNR) is required. In this study, the authors develop a shrinkage-based SNR estimator in the data-aided and non-dataaided schemes for higher M-ary phase-shift-keying (M ≥ 8) and quadrature amplitude modulations. The observed Cramér-Rao lower bound is used as the variance of the expectati...

This article studies the limiting behavior of a robust M-estimator of population covariance matrices as both the number of available samples and the population size are large. Using tools from random matrix theory, we prove that the difference between the sample covariance matrix and (a scaled version of) the robust M-estimator tends to zero in spectral norm, almost surely. This result is appli...

We ask whether or not the saddlepoint property holds. for robust M-estimation of scale, in gross-errors and Kolmogorov neighbourhoods of certain distributions. This is of interest since the saddlepoint property implies the minimax property that the supremum of the asymptotic variance of an M-estimator is minimized by the maximum likelihood estimator for that member of the distributional class w...

c This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. 2 Abstract A large number of radio resource management algorithms rely on accurate measurements of the signal to interference ratio (SIR). We propose a simple, fast SIR estimator for narrow-band M-PSK modulated signals (M > 2). T...

Sensitivity analysis is a powerful tool in determining parameters to which the system output is most responsive, in assessing robustness of the system to extreme circumstances or unusual environmental conditions, in identifying rate limiting pathways as a candidate for drug delivery, and in parameter estimation for calculating the Hessian of the objective function. Anderson [SIAM J. Numer. Anal...

Introduction: In burst-mode digital transmission systems adopting coherent demodulation, phase recovery within each burst becomes a crucial issue. Among the blind (or non-data-aided) carrier phase estimators available from the literature, perhaps the fourth power estimate appears to be the most popular. It is known to yield an approximate maximum likelihood estimator in the limit of small SNR [...

Robust alternatives to the seemingly unrelated regression (SUR) estimator of Zellner (1962) are proposed for the classical multivariate regression model. These weighted M-estimators achieve an asymptotic covariance matrix analogous to that of the SUR estimator. Comparisons are made with the efficient estimator in the case of elliptically contoured distributions. The / lt least absolute deviatio...