A note on the strong consistency of least squares estimates
The strong consistency of least squares estimates in multiples regression models with i.i.d. errors is obtained under assumptions on the design matrix and moment restrictions on the errors.
A Note on Three-Stage Confidence Intervals for the Difference of Locations: The Exponential Case.
A predictive approach to some hypothesis testing problems.
A Probability Inequality for Ranges and its Application to Maximum Range Test Procedures.
A Proof of Asymptotic Normality for some VARX Models.
Time Series with Approximated Beta Marginal
A regression characterization of inverse Gaussian distributions and application to EDF goodness-of-fit tests.
A remark on existence of statistical functionals
A Remark on Lehmann-Unbiased Estimators for Scale Resp. Location Parameters.
A Remark on Robustness of Linear Best Estimates.
A Representation Theorem for a Convex Cone of Quasi Convex Functions.
A robust test for variance
A sampling scheme for reliability estimation.
A sequential Bayesian approach to estimating the dimension of a multinomial distribution
A sharp analysis on the asymptotic behavior of the Durbin–Watson statistic for the first-order autoregressive process
The purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin–Watson statistic. We focus our attention on the first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We establish the almost sure convergence and the asymptotic normality for both the least squares estimator of the unknown parameter of the autoregressive process as well as for the serial correlation estimator associated with the driven noise....
A simple robust estimator of correlations for Gaussian stationary random sequences
A simple technique for proving unbiasedness of tests and confidence regions
A study of the number of solutions of the system of the log-likelihood equations for the 3-parameter Weibull distribution
The maximum likelihood estimators of the parameters for the 3-parameter Weibull distribution do not always exist. Furthermore, computationally it is difficult to find all the solutions. Thus, the case of missing some solutions and among them the maximum likelihood estimators cannot be excluded. In this paper we provide a simple rule with help of which we are able to know if the system of the log-likelihood equations has even or odd number of solutions. It is a useful tool for the detection of all...
A study of the tangent space model of the von Mises-Fisher distrubution.
For a random rotation X = M0 eφ(ε) where M0 is a 3 x 3 rotation, ε is a trivariate random vector, and φ(ε) is a skew symmetric matrix, the least squares criterion consists of seeking a rotation M called the mean rotation minimizing tr[(M - E(X))t (M - E(X))]. Some conditions on the distribution of ε are set so that the least squares estimator is unbiased. Of interest is when ε is normally distributed N(0;Σ). Unbiasedness of the least squares estimator is dealt with according to eigenvalues of Σ.
A sufficient statistic and a nonstandard linearization in nonlinear regression