Godambe (1960, 1985) and Hansen (1982) proposed the method of estimating function which makes a bridge between least squares estimator and maximum likelihood estimator. In this paper we apply the estimating function approach to CHARN models which include many well-known nonlinear time series models as special cases. The innovation density is permitted to be skew-symmetric. Since the estimation ...

Abstract: In this article asymptotic expressions for the final prediction error (FPE) and the accumulated prediction error (APE) of the least squares predictor are obtained in regression models with nonstationary regressors. It is shown that the term of order 1/n in FPE and the term of order log n in APE share the same constant, where n is the sample size. Since the model includes the random wa...

Generalized least squares (GLS) regional regression procedures have been developed for estimating river flow quantiles. A widely used GLS procedure employs a simplified model error structure and average covariances when constructing an approximate residual error covariance matrix. This paper compares that GLS estimator (b̂GLS MC ), an idealized GLS estimator (b̂GLS E ) based on the simplifying as...

The hyperspectral imaging technology is used to detect early-maturing pear’s effective acidity nondestructively, and effective prediction model is established. 145 pears’ hyperspectral images are obtained in the wavelength range of 400nm-1000nm. Total 145 pears are separated into the calibration set (77 samples) and prediction set (68 samples). Early-maturing pear’s effective acidity partial le...

Diierent predictors and their approximators in nonlinear prediction regression models are studied. The minimal value of the mean squared error (MSE) is derived. Some approximate formulae for the MSE of ordinary and weighted least squares predictors are given.

The problem of estimating the coeecients in a linear regression model is considered when some of the response values are missing. The conventional Yates procedure employing least squares predictions for missing values does not lead to any improvement over the least squares estimator using complete observations only. However, if we use Stein-rule predictions , it is demonstrated that some improv...

The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including...

The digital Fourier transform (DFT) and the adaptive least mean square (LMS) algorithm have existed for some time. This paper establishes a connection between them. The result is the “LMS spectrum analyzer,” a new means for the calculation of the DFT. The method uses a set of N periodic complex phasors whose frequencies are equally spaced from dc to the sampling frequency. The phasors are weigh...

Spatial diversity technique enables improvement in quality and reliability of wireless link. Antenna diversity along with understanding effects of channel on transmitted signal and methods to overcome the channel impairment plays an important role in wireless communication where sharing of channel occurs between users. In this paper single input single output system (SISO) is compared with mult...

3rd generation partnership project (3GPP) long term evolution (LTE) uses single carrier-frequency division multiple access (SC-FDMA) in uplink transmission and orthogonal frequency division multiple access (OFDMA) scheme for the downlink. One of the most important challenges for a transceiver design is channel estimation (CE) and equalization. In this paper, a training based least mean square (...