We use the sample covariation to develop asymptotic tests for independence for data in the normal domain of attraction of a stable law. The tests can be used for finite or infinite variance processes. In a simulation study we compare the finite sample performance of the proposed tests to the Portmanteau test commonly used in time series modeling. The null convergence of the test statistics to t...

This paper introduces and analyzes the asymptotic (large sample) performance of a family of blind feedforward nonlinear least-squares (NLS) estimators for joint estimation of carrier phase, frequency offset, and Doppler rate for burst-mode phaseshift keying transmissions. An optimal or “matched” nonlinear estimator that exhibits the smallest asymptotic variance within the family of envisaged bl...

The skip list is a list-based data structure introduced some years ago by Pugh [12]. In [6] an optimized version of the skip list search algorithm has been investigated and an asymptotic result on the variance of the total unsuccessful search cost has been derived. Here we give the precise asymptotics for the difference of the variance of this parameter and the variance of the total successful ...

The efficiency of single-nucleotide polymorphism haplotype analysis may be increased by DNA pooling, which can dramatically reduce the number of genotyping assays. We develop a method for obtaining maximum likelihood estimates of haplotype frequencies for different pool sizes, assess the accuracy of these estimates, and show that pooling DNA samples is efficient in estimating haplotype frequenc...

When using maximum likelihood methods to estimate genetic and environmental components of (co)variance, it is common to test hypotheses using likelihood ratio tests, since such tests have desirable asymptotic properties. In particular, the standard likelihood ratio test statistic is assumed asymptotically to follow a chi2 distribution with degrees of freedom equal to the number of parameters te...

Fan, Heckman and Wand (1995) proposed locally weighted kernel polynomial regression methods for generalized linear models and quasilikelihood functions. When the covariate variables are missing at random, we propose a weighted estimator based on the inverse selection probability weights. Distribution theory is derived when the selection probabilities are estimated nonparametrically. We show tha...

The main results of this article are asymptotic formulas for the variance of the number of zeros of a Gaussian random polynomial of degree N in an open set U ⊂ C as the degree N → ∞, and more generally for the zeros of random holomorphic sections of high powers of any positive line bundle over any Riemann surface. The formulas were conjectured in special cases by Forrester and Honner. In higher...

The Quality-Adjusted Time Without Symptoms or Toxicity (Q-TWiST) statistic previously introduced by Glasziou, Simes and Gelber (1990, Statistics in Medicine 9, 1259-1276) combines toxicity, disease-free survival, and overall survival information in assessing the impact of treatments on the lives of patients. This methodology has received positive reviews from clinicians as intuitive and useful,...

We consider the least-squares regression problem and provide a detailed asymptotic analysis of the performance of averaged constant-step-size stochastic gradient descent. In the strongly-convex case, we provide an asymptotic expansion up to explicit exponentially decaying terms. Our analysis leads to new insights into stochastic approximation algorithms: (a) it gives a tighter bound on the allo...

This paper provides asymptotic bias and variance analysis for MIMO system estimates obtained by using generalizations of FIR model structures and least squares techniques. The generalizations are such that prior approximate knowledge of the system poles may be incorporated. The obtained variance expressions provide extensions to well known results that have previously been derived only for FIR ...