Stefania Naddeo

Two-stage adaptive cluster sampling

Adaptive cluster sampling is usually applied when estimating the abundance of elusive, clustered biological populations. It is commonly supposed that all individuals in the selected area units are detected by the observer, but in many acutal situations this assumption may be highly unrealistic and some individuals may be missed. This paper deals with the problem of handling imperfect detectability in adaptive cluster sampling by using a pure design-based approach. A two-stage adaptive procedure is proposed where the abundance in the selected units is estimated by replicated counts.

Design-based inference on Bernstein type estimators for continuous populations

The estimation of the values of a variable at any point of a study area is performed using Bernstein polynomials when the sampling scheme is implemented by selecting a point in each polygon of a regular grid overimposed onto the area. The evaluation of the precision of the resulting estimates is investigated under a completely design-based framework. Moreover, as the main contribution to the mean squared error of the Bernstein-type estimator is due to the bias, also a pseudo-jackknife estimator is proposed. The performance of both estimators is investigated theoretically and by means of a simulation study. An application to a soil survey performed in Berkshire Downs in Oxfordshire (UK) is considered.

Exact Bayesian Higher Posterior Density Interval for the Correlation Coefficient of a Normal Bivariate Distribution

Abstract This paper proposes a new procedure for computing the extremes of the highest posterior density (H.P.D.) interval of the correlation coefficient of a normal bivariate distribution. The procedure uses the exact expression of the correlation coefficient distribution function, which is based on noninformative priors of the parameters of the normal distribution, and avoids the numerical integration required by the commonly adopted expression. The extremes of the highest posterior density interval are implemented as routines in the symbolic programming language Mathematica.