An m-sample semiparametric model in which the ratio of m − 1 probability density functions with respect to the mth is of a known parametric form without reference to any parametric model is considered. This model arises naturally from retrospective studies and multinomial logistic regression model. A projection density estimator is constructed by smoothing the increments of the maximum semipara...

E{S[m, I ] S*[m', l ' ] } = C[m, I ] 6[m m'] 6 [ l 1 ' 1 , (1) We propose an unbiased, computationally efficient estimator of the scattering function that is specifically suited to underspread WSSUS channels. The novel estimator allows for nonideal sounding signals, fast time-variation, and on-line where E denotes expectation, s[m, I1 = czzi h[n, m1 e-j21rln" is the (delay-Doppler) spreading fu...

Meta learning of optimal classifier error rates allows an experimenter to empirically estimate the intrinsic ability of any estimator to discriminate between two populations, circumventing the difficult problem of estimating the optimal Bayes classifier. To this end we propose a weighted nearest neighbor (WNN) graph estimator for a tight bound on the Bayes classification error; the Henze-Penros...

Suppose the random vector (X,Y ) satisfies the regression model Y = m(X) + σ(X)ε, where m(·) = E(Y |·), σ2(·) = Var(Y |·) and ε is independent of X. The covariate X is d-dimensional (d ≥ 1), the response Y is one-dimensional, and m and σ are unknown but smooth functions. In this paper we study goodness-of-fit tests for the parametric form of the error distribution under this model, without assu...

The study provides a statistical trend analysis of different air pollutants using Mann-Kendall and Sen’s slope estimator approach on past pollutants statistics from air quality index station of Varanasi, India. Further, using autoregressive integrated moving average model, future values of air pollutant levels are predicted. Carbon monoxide, nitrogen dioxide, sulphur dioxide, particu...