-
作者：NAGARAJ, NK; FULLER, WA
作者单位：Iowa State University
摘要：Least squares estimators of the parameters of a linear time series model, where the parameters are constrained by a set of nonlinear restrictions, are studied. The model may contain lags of the dependent variable as regressors and the sums of squares of the explanatory variables may grow at different rates as the sample size increases. The estimation procedures can be applied to a regression model with an error process that satisfies either a stationary or a nonstationary autoregression.
-
作者：THOMASAGNAN, C
作者单位：Universite de Toulouse; Universite Toulouse 1 Capitole
摘要：Some relationships have been established between unbiased linear predictors of processes, in signal and noise models, minimizing the predictive mean square error and some smoothing spline functions. We construct a new family of multidimensional splines adapted to the prediction of locally homogeneous random fields, whose m-spectral measure (to be defined) is absolutely continuous with respect to Lebesgue measure and satisfies some minor assumptions. By considering partial splines, one may incl...
-
作者：KHAN, RA
摘要：Let {X(n), n greater-than-or-equal-to 1} be a sequence of random variables and let P-theta be a probability measure under which (X1, ..., X(n)) have joint pdf's f(n)(X1,..., X(n),theta)= L(n)(theta), n greater-than-or-equal-to 1. Suppose u(n) = u(n)(X1,..., X(n)), n greater-than-or-equal-to 1, are statistics such that (u(n) - c)(L(n)(theta') - L(n)(theta)) greater-than-or-equal-to 0, for all inverted A (X1,...,X(n)) n greater-than-or-equal-to 1, for some constant c = c(theta, theta'), theta no...
-
作者：PUKELSHEIM, F; TORSNEY, B
作者单位：University of Glasgow
摘要：An explicit formula is derived to compute the A-optimal design weights on linearly independent regression vectors, for the mean parameters in a linear model with homoscedastic variances. The formula emerges as a special case of a general result which holds for a wide class of optimality criteria. There are close links to iterative algorithms for computing optimal weights.
-
作者：FAN, JQ
摘要：We discuss the difficulties of estimating quadratic functionals based on observations Y(t) from the white noise model Y(t) = integral 0t(f)(u) du + sigma-W(t), t is-an-element-of [0, 1], where W(t) is a standard Wiener process on [0, 1]. The optimal rates of convergence (as sigma --> 0) for estimating quadratic functionals under certain geometric constraints are found. Specifically, the optimal rates of estimating integral 0(1)[f(k)(x)]2 dx under hyperrectangular constraints SIGMA = {f: \x(j)(...
-
作者：LAI, TL; YING, ZL
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Buckley and James proposed an extension of the classical least squares estimator to the censored regression model. It has been found in some empirical and Monte Carlo studies that their approach provides satisfactory results and seems to be superior to other extensions of the least squares estimator in the literature. To develop a complete asymptotic theory for this approach, we introduce herein a slight modification of the Buckley-James estimator to get around the difficulties caused by the i...
-
作者：BHANSALI, RJ; PAPANGELOU, F
作者单位：University of Manchester
摘要：Given a realization of T consecutive observations of a stationary autoregressive process of unknown, possibly infinite, order m, it is assumed that a process of arbitrary finite order p is fitted by least squares. Under appropriate conditions it is known that the estimators of the autoregressive coefficients are asymptotically normal. The question considered here is whether the moments of the (scaled) estimators converge, as T --> infinity, to the moments of their asymptotic distribution. We e...
-
作者：BICKEL, PJ; RITOV, Y; WELLNER, JA
作者单位：Hebrew University of Jerusalem; University of Washington; University of Washington Seattle
摘要：Suppose that P is the distribution of a pair of random variables (X, Y) on a product space X x Y with known marginal distributions P(X) and P(Y). We study efficient estimation of functions theta(h) = integral h dP for fixed h: X x Y --> R under iid sampling of (X, Y) pairs from P and a regularity condition on P. Our proposed estimator is based on partitions of both X and Y and the modified minimum chi-square estimates of Deming and Stephan (1940). The asymptotic behavior of our estimator is go...
-
作者：FAN, JQ
摘要：Deconvolution problems arise in a variety of situations in statistics. An interesting problem is to estimate the density f of a random variable X based on n i.i.d. observations from Y = X + epsilon, where epsilon is a measurement error with a known distribution. In this paper, the effect of errors in variables of nonparametric deconvolution is examined. Insights are gained by showing that the difficulty of deconvolution depends on the smoothness of error distributions: the smoother, the harder...
-
作者：COX, DD
摘要：An asymptotic analysis is presented for estimation in the three-parameter first-order autoregressive model, where the parameters are the mean, autoregressive coefficient and variance of the shocks. The nearly nonstationary asymptotic model is considered wherein the autoregressive coefficient tends to 1 as sample size tends to infinity. Three different estimators are considered: the exact Gaussian maximum likelihood estimator, the conditional maximum likelihood or least squares estimator and so...
