In this paper we derive some unbiased estimators of the population mean under simple inverse sampling with replacement, using the class of Hansen-Hurwitz and Horvitz-Thompson type estimators and the post-stratification approach. We also compare the efficiency of resulting estimators together with Murthy's estimator. We show that in despite of general belief, the strategy consisting of inverse s...

We consider a class of semi-parametric dynamic models with strong white noise errors. This processes includes the standard Vector Autoregressive (VAR) model, nonfundamental structural VAR, mixed causal-noncausal models, as well nonlinear such (multivariate) ARCH-M model. For estimation in this class, we propose Generalized Covariance (GCov) estimator, which is obtained by minimizing residual-ba...

 This paper considers the problem of estimating the population mean Ybar of the study variate Y using information on different parameters such as population mean $(bar{X})$, coefficient of variation $(C_x)$, kurtosis  $beta_{2(x)}$, standard deviation $(S_x)$ of the auxiliary variate x and on the correlation coefficient, $rho$, between the study variate $Y$ and the auxiliary variate $...

In this paper, a new control chart to monitor multi-binomial processes is first proposed based on a transformation method. Then, the maximum likelihood estimators of change points designed for both step changes and linear-trend disturbances are derived. At the end, the performances of the proposed change-point estimators are evaluated and are compared using some Monte Carlo simulation experimen...

‎In this article‎, ‎we develop two nonparametric smoothing estimators for parameter of a time-variant parametric model‎. ‎This parameter can be from any parametric family or from any parametric or semi-parametric regression model‎. ‎Estimation is based on a two-step procedure‎, ‎in which we first get the raw estimate of the parameter at a set of disjoint time...