Conceptual cost estimation is an important step in project feasibility decisions when there not enough information on detailed design and requirements. Methods that enable quick reasonably accurate conceptual estimates are crucial for achieving successful the early stages of construction projects. For this reason, numerous machine learning methods proposed literature use different mechanisms. I...

This paper introduces a bootstrap procedure, accounting for data dependence and parameter estimation error, which facillitates the construction of parametric speci ̄cation tests of di®usion processes. The bootstrap method hinges on a twofold extension of the Politis and Romano (1994) stationary bootstrap. First we provide an empirical process version of this bootstrap, and second, we account for...

Motivated by a recent study of Bao and Ullah (2007a) on finite sample properties of MLE in the pure SAR (spatial autoregressive) model, a general method for third-order bias and variance corrections on a nonlinear estimator is proposed based on stochastic expansion and bootstrap. Working with concentrated estimating equation simplifies greatly the high-order expansions for bias and variance; a ...

This paper considers the use of the m out of n bootstrap (Bickel, Goetze and van Zwet, 1994) in setting critical values for a quite general class of hypothesis testing problems. We show that the usual n out of n nonparametric bootstrap generally fails to estimate the null distribution of the test statistics, and that if the m o(n) out of n bootstrap is used to set the critical value of the test...