This paper studies the limiting behavior of general functionals of order statistics and their multivariate concomitants for weakly dependent data. The asymptotic analysis is performed under a conditional moment-based notion of dependence for vector-valued time series. It is argued, through analysis of various examples, that the dependence conditions of this type can be effectively implied by other dependence formations recently proposed in time-series analysis, thus it may cover many existing linear and nonlinear processes. The utility of this result is then illustrated in deriving the asymptotic properties of a semiparametric estimator that uses the k-Nearest Neighbour estimator of the inverse of a multivariate unknown density. This estimator is then used to calculate consumer surpluses for electricity demand in Ontario for the period 1971 to 1994. A Monte Carlo experiment also assesses the efficacy of the derived limiting behavior in finite samples for both these general functionals and the proposed estimator.

Additional Metadata
Keywords Consumer surplus, K-nearest neighbour, Multivariate concomitant, Order statistics, Semiparametric estimation
Journal Sankhya: The Indian Journal of Statistics
Citation
Chu, B, Huynh, K.P. (Kim P.), & Jacho-Chávez, D.T. (David T.). (2013). Functionals of order statistics and their multivariate concomitants with application to semiparametric estimation by nearest neighbours. Sankhya: The Indian Journal of Statistics, 75 B(PART 2), 238–292.