Estimator

Color flow mapping systems have become widely used in the short time since their development. These systems overlay a pseudo-color velocity map upon the gray-scale two-dimensional image. Between 4 and 16 pulses are directed to each line-of-sight, and this requirement reduces the frame rate in comparison with the gray-scale image. Other limitations of color flow mapping include its ability to estimate only the velocity toward or away from the transducer and an increase in the variance in comparison with spectral Doppler. Potential artifacts include aliased velocities and the detection of flow in hypoechoic or hyperechoic nonvascular structures. Clinical applications include cardiology, studies of the abdominal and peripheral vasculature, evaluation of organ perfusion and the differentiation of tumors. Most current systems use narrowband estimators that examine a fixed sample volume and detect a change in phase between two pulses. Wideband estimators that can track red blood cells in two or three dimensions are under evaluation. Narrowband estimators, including the autocorrelator, the short Fourier transform and second order autoregressive filters, are compared with wideband estimators including cross-correlation, sum-absolute-difference and the wideband maximum likelihood estimator. Because the intensity of blood echoes is far smaller than echoes from surrounding tissue, high pass filters have been developed that can reject the larger signal from tissue using the return from a small number of pulses. Other areas of research include strategies for flow estimation with contrast agents, three-dimensional color flow mapping and power Doppler flow mapping.

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

There are three nonparametric regression methods covered in this section. These are Moving Average Filtering-Based Smoothing, Local Regression Smoothing, and Kernel Smoothing Methods. The Moving Average Filtering-Based Smoothing methods discussed here are Moving Average Filtering and Savitzky-Golay Filtering. While, the Local Regression Smoothing techniques involved here are Lowess and Loess. In this type of smoothing, Robust Smoothing and Upper-and-Lower Smoothing are also explained deeply, related to Lowess and Loess. Finally, the Kernel Smoothing Method involves three methods discussed. These are Nadaraya-Watson Estimator, Priestley-Chao Estimator, and Local Linear Kernel Estimator. The advantages of all above methods are discussed as well as the disadvantages of the methods.

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observable covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design that are of practical importance, like sample size, the type of the outcome variable, and aspects of the selection process. We find that trimming individual observations with too much weight as well as the choice of tuning parameters is important for all estimators. The key conclusion from our simulations is that a particular radius matching estimator combined with regression performs best overall, in particular when robustness to misspecifications of the propensity score is considered an important property.

The focus of this study is on the development of a mathematical model for estimating tension of a printing section by using the register error in R2R (Roll to Roll) e-Printing systems. In a printing section of conventional R2R printing systems, the tension is ...

As the concept of methodology has advanced, varied methods of estimating residuals have been developed including regression method, Bartlett’s method and Anderson-Rubin’s method. The study utilized estimation maximization approach together with other methods of estimating residuals under the structural equation model. The results showed that the strength of the existing methods in structural equation modelling are the weaknesses of the estimation maximization method, and vice versa. It was, therefore, found that from the comparative model fit information that the Bartlett’s based method gave better residual parameter estimates compared to the Regression based and the Anderson Rubin based methods. However, the estimation maximization method gave better residual parameter estimates than the other three existing methods; the Regression, Bartlett’s and the Anderson Rubin based methods.

In this paper, an Artificial Neural Network (ANN) for accurate estimation of the speed and flux for induction motor (IM) drives has been presented for industrial applications such as electric vehicles (EVs). Two ANN estimators have been designed, one for the rotor speed estimation and the other for the stator and rotor flux estimation. The input training data has been collected based on the currents and voltage data, while the output training data of the speed and stator and rotor fluxes has been established based on the measured speed and flux estimator-based mathematical model of the IM. The designed ANN estimators can overcome the problem of the parameter’s variations and drift integration problems. Matlab/Simulink has been used to develop and test the ANN estimators. The results prove the ANN estimators’ effectiveness under various operation conditions.

In this paper, an estimator wh ich is robust in the present of outlier and Skewness is proposed. This is achieved by incorporating median, a good measure of location in this regard to the modified ratio estimator developed as in(22). Using well analy zed data to illustrate the procedure for the Ratio estimator, its Mean square error was observed the minimu m of the existing estimators considered. The proposed modified estimator is uniformly better than all other estimators and thus most preferred over the existing modified ratio estimators for the use in practical applications for certain population with peculiar characteristics

Abstract: A high degree of multicollinearity among the explanatory variables severely impairs estimation of regression coefficients by the Ordinary Least Squares. Several methods have been suggested to ameliorate the deleterious effects of multicollinearity.

Most standard methods based on maximum likelihood (ML) estimates of power-law exponents can only be reliably used to identify exponents smaller than minus one. The argument that power laws are otherwise not normalizable, depends on the underlying sample space the data is drawn from, and is true only for sample spaces that are unbounded from above. Power-laws obtained from bounded sample spaces (as is the case for practically all data related problems) are always free of such limitations and maximum likelihood estimates can be obtained for arbitrary powers without restrictions. Here we first derive the appropriate ML estimator for arbitrary exponents of power-law distributions on bounded discrete sample spaces. We then show that an almost identical estimator also works perfectly for continuous data. We implemented this ML estimator and discuss its performance with previous attempts. We present a general recipe of how to use these estimators and present the associated computer codes.

Auxiliary information is often used to improve the precision of estimators of finite population total. Calibration approach is widely used for making efficient use of auxiliary information in survey estimation. We proposed the regression type estimators of the population total using the calibration approach under the assumption that the population level auxiliary information is available at secondary stage unit level under two stage sampling design. Through Simulation it was found that all the proposed estimators are performing better than the usual Horvitz-Thompson estimators under two stage sampling design.

The problem of obtaining better ratio estimators of the population means are dominating in survey sampling. This paper provides a modified class of exponential type estimators using combinations of some existing estimators. Expressions for the bias and Mean Square Error (MSE) with the optimality conditions for this class of estimators have been established. Both analytical and numerical comparison with some existing estimators shows better performances from members of the proposed class.

This paper is an effort to investigate the effect of non-response at the current (second) occasion in successive sampling. Using the subsampling non-respondent procedure, some exponential-type estimators have been proposed to estimate the current population mean. Properties of the proposed class of estimators are examined, and respective optimum replacement strategies are derived. Empirical studies are carried out to evaluate the performances of the suggested estimators. The outcomes are interpreted, and appropriate recommendations have been made.

Given a stationary multidimensional spatial process (Z i = (X i , Y i ) ∈ ℝ d × ℝ, i ∈ ℤ N ), we investigate a kernel estimate of the spatial conditional quantile function of the response variable Y i given the explicative variable X i . Almost complete convergence and consistency in L 2r norm (r ∈