Suppose the random vector (X,Y ) satisfies the regression model Y = m(X) + σ(X)ε, where m(·) = E(Y |·) belongs to some parametric class {mθ(·) : θ ∈ Θ} of regression functions, σ2(·) = Var(Y |·) is unknown, and ε is independent of X. The response Y is subject to random right censoring, and the covariate X is completely observed. A new estimation procedure for the true, unknown parameter vector ...

We consider the problem of estimating the slope parameter in circular functional linear regression, where scalar responses Y1, . . . , Yn are modeled in dependence of 1periodic, second order stationary random functions X1, . . . , Xn. We consider an orthogonal series estimator of the slope function β, by replacing the first m theoretical coefficients of its development in the trigonometric basi...

S U M M A R Y Robust magnetotelluric response function estimators are now in standard use in electromagnetic induction research. Properly devised and applied, these have the ability to reduce the influence of unusual data (outliers) in the response (electric field) variables, but are often not sensitive to exceptional predictor (magnetic field) data, which are termed leverage points. A bounded ...

Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. In many practical situations, is known in advance to lie in an interval, say for some In this case, the maximum likelihood estimator...

A bstructThis paper discusses methods for the estimation of the autocorrelation coefficients of a finite-dependent stationary random sequence. Three estimators are examined: the sample average and two proposed approaches, namely the pseudomaximum-likelihood (pseudo-ML) estimator and the pseudo-M estimator. The latter scheme is found as a solution of a Fredholm integral equation. All three estim...

ÐThe robust Huber M-estimator, a differentiable cost function that is quadratic for small errors and linear otherwise, is modeled exactly, in the original primal space of the problem, by an easily solvable simple convex quadratic program for both linear and nonlinear support vector estimators. Previous models were significantly more complex or formulated in the dual space and most involved spec...

Many different robust estimation approaches for the covariance or shape matrix of multivariate data have been established until today. Tyler’s M-estimator has been recognized as the ‘most robust’ M-estimator for the shape matrix of elliptically symmetric distributed data. Tyler’s Mestimators for location and shape are generalized by taking account of incomplete data. It is shown that the shape ...

We consider the problem of estimating the gains and phases of the RF channels of a M-element transmitting array, based on a calibration procedure where M orthogonal signals are sent through M orthogonal beams and received on a single antenna. The received data vector obeys a linear model of the type y 1⁄4 AFg þ n where A is an unknown complex scalar accounting for propagation loss and g is the ...

The problem of estimating mean and covariances of a multivariate normally distributed random vector has been studied in many forms. This paper focuses on the estimators proposed by Ohlson et al. (2011) for a banded covariance structure with m-dependence. We rewrite the estimator when m = 1, which makes it easier to analyze. This leads to an adjustment, and an unbiased estimator can be proposed....