ESTIMATION OF THE OFFSPRING MEAN IN A SUPERCRITICAL OR NEAR-CRITICAL SIZE-DEPENDENT BRANCHING PROCESS.

We consider a single-type supercritical or near-critical size-dependent branching process {N_n}_n such that the offspring mean converges to a limit m ≥ 1 with a rate of convergence of order N^α _n as the population size N_n grows to : ∞ and the variance may vary at the rate N^β _n, where -1 ≤ β < 1. The offspring mean m(N) = m + μN^-α + 0(N^-α) depends on an unknown parameter θ₀ belonging either to the asymptotic model (θ₀ = m) or to the transient model (θ₀ = p. We estimate θ₀ on the nonextinction set from the observations {N_h, ..., N_n,} by using the conditional least-squares method weighted by {N^-y _k-1}k (where γ &epsiv; &Rscript;) in the approximate model m_θ &vcirc;_n (·), where &vcirc;_n is any estimation of the parameter of the nuisance part (O (N^-α) if θ₀ = μ). We study the strong consistency of the estimator of θ₀ as γ varies, with either h or n - h remaining constant as n → ∞. We use either a minimum-contrast method or a Taylor approximation of the first derivative of the contrast. The main condition for obtaining strong consistency concerns the asymptotic behavior of the process. We also give the asymptotic distribution of the estimator by using a central-limit theorem for random sums and we show that the best rate of convergence is attained when γ = 1 + β. [ABSTRACT FROM AUTHOR]