4期

最新文章

• Multivariate tests based on left-spherically distributed linear scores (vol 26, pg 1972, 1998) AI翻译
  作者：Läuter, J; Glimm, E; Kropf, S
  作者单位：University Hospital Magdeburg
• Blocking in regular fractional factorials: A projective geometric approach AI翻译
  作者：Mukerjee, R; Wu, CFJ
  作者单位：Indian Institute of Management (IIM System); Indian Institute of Management Calcutta; University of Michigan System; University of Michigan
  摘要：A projective geometric characterization is given of the existence of any regular main effect s(n-k) design in s(y) blacks It leads to a constructive method for finding a maximal blocking scheme for any given fractional factorial design. A useful sufficient condition for admissible block designs is given in terms of the minimum aberration property of a certain unblocked design.
• Asymptotic normality of the L1 error of the Grenander estimator AI翻译
  作者：Groeneboom, P; Hooghiemstra, G; Lopuhaä, HP
  作者单位：Delft University of Technology
  摘要：Groeneboom introduced a jump process that can be used (among other things) to study the asymptotic properties of the Grenander estimator of a monotone density. In this paper we derive the asymptotic normality of a suitably rescaled version of the L-1 error of the Grenander estimator, using properties of this jump process.
• Asymptotic distribution of the reduced rank regression estimator under general conditions AI翻译
  作者：Anderson, TW
  作者单位：Stanford University
  摘要：In the regression model Y = eta + BX + Z with Z unobserved, EZ = 0 and EZZ' = Sigma(ZZ), the least squares estimator of B is (B) over cap = SYXSXX-1. If the rank of B is known to be k less than the dimensions of Y and X, the reduced rank regression estimator of B, say (B) over cap(k), depends on the first k canonical variates of Y and X. The asymptotic distribution of (B) over cap(k) is obtained and compared with the asymptotic distribution of (B) over cap(k). The advantage of (B) over cap(k) ...
• Strong consistency of maximum quasi-likelihood estimators in generalized linear models with fixed and adaptive designs AI翻译
  作者：Chen, KN; Hu, IC; Ying, ZL
  作者单位：Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; Rutgers University System; Rutgers University New Brunswick
  摘要：Strong consistency for maximum quasi-likelihood estimators of regression parameters in generalized Linear regression models is studied. Results parallel to the elegant work of Lai, Robbins and Wei and Lai and Wei on least squares estimation under both fixed and adaptive designs are obtained. Let y(1),..., y(n) and x(1),...,x(n) be the observed responses and their corresponding design points (p x 1 vectors), respectively. For fixed designs, it is shown that if the minimum eigenvalue of Sigma x(...
• Tests of goodness of fit based on the L2-Wasserstein distance AI翻译
  作者：Del Barrio, E; Cuesta-Albertos, JA; Matrán, C; Rodríguez-Rodríguez, JM
  作者单位：Universidad de Valladolid; Universidad de Cantabria
  摘要：Ne consider the Wasserstein distance between a sample distribution and the set of normal distributions as a measure of nonnormality. By considering the standardized version of this distance me obtain a version of Shapiro-Wilk's test of normality. The asymptotic behavior of the statistic is studied using approximations of the quantile process by Brownian bridges. This method differs from the ad hoc method of de Wet and Venter and permits a similar analysis for testing other location scale famil...
• Mode testing in difficult cases AI翻译
  作者：Cheng, MY; Hall, P
  作者单位：National Taiwan University; Australian National University
  摘要：Usually when testing the null hypothesis that a distribution has one mode against the alternative that it has two, the null hypothesis is interpreted as entailing that the density of the sampling distribution has a unique point of zero slope, which is a local maximum. In this paper we argue that a more appropriate null hypothesis is that the density has two paints of zero slope, of which one is a local maximum and the other is a shoulder. We show that when a test for a mode-with-shoulder is pr...
• Optimal tests for autoregressive models based on autoregression rank scores AI翻译
  作者：Hallin, M; Jurecková, J
  作者单位：Universite Libre de Bruxelles; Charles University Prague
  摘要：Locally asymptotically optimal tests based on autoregression rank scores are constructed for testing Linear constraints on the structural parameters of AR processes. Such tests are asymptotically distribution free and do not require the estimation of nuisance parameters. They constitute robust, flexible and quite powerful alternatives to existing methods such as the classical correlogram-based parametric tests, the Gaussian Lagrange multiplier tests, the optimal non-Gaussian and ranked residua...
• Some theoretical results for fractional factorial split-plot designs AI翻译
  作者：Bingham, DR; Sitter, RR
  作者单位：Simon Fraser University
  摘要：Fractional factorial (FF) designs are commonly used in industrial experiments to identify factors affecting a process. When it is expensive or difficult to change the levels of some of the factors, fractional factorial split-plot (FFSP) designs represent a practical design option. Though FFSP design matrices correspond to FF design matrices, the randomization structure of the FFSP design is different. In this paper we discuss the impact of randomization restrictions on the choice of FFSP desig...
• Optimal designs for rational models and weighted polynomial regression AI翻译
  作者：Dette, H; Haines, LM; Imhof, L
  作者单位：Ruhr University Bochum; University of Kwazulu Natal; RWTH Aachen University
  摘要：In this paper D-optimal designs for the weighted polynomial regression model of degree p with efficiency function (1 + x(2))(-n) are presented. Interest in these designs stems from the fact that they are equivalent to locally D-optimal designs for inverse quadratic polynomial models. For the unrestricted design space R and p < n, the D-optimal designs put equal masses on p + 1 points which coincide with the zeros of an ultraspherical polynomial, while for p = n they are equivalent to D-optimal...