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[PDF] Top 20 Difference based M estimator of generalized semiparametric model with NSD errors

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Difference based M estimator of generalized semiparametric model with NSD errors

Difference based M estimator of generalized semiparametric model with NSD errors

... the generalized partially linear model and partially linear single-index model (h( · ) is an unknown link function) are also derived from the partially linear ...for generalized partially ... See full document

15

Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation

Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation

... linear model by using the differences technique to estimate the parametric part and using Nadaria Watson's estimator to estimate the nonparametric part we found the following: 1-When the sample size n = 50, ... See full document

10

A difference based approach in the partially linear model with dependent errors

A difference based approach in the partially linear model with dependent errors

... In this paper, by a difference-based approach, we will use the ordinary least square and wavelet to investigate model (1). The differencing procedures provide a convenient means for introducing nonparametric ... See full document

16

Asymptotic properties of wavelet estimators in heteroscedastic semiparametric model based on negatively associated innovations

Asymptotic properties of wavelet estimators in heteroscedastic semiparametric model based on negatively associated innovations

... In this paper, we aim to derive the least squares estimators, weighted least squares esti- mators of β, and their strong consistency for the wavelet estimators of f and g. At the same time, Berry–Esséen-type bounds of ... See full document

21

The large deviation for the least squares estimator of nonlinear regression model based on WOD errors

The large deviation for the least squares estimator of nonlinear regression model based on WOD errors

... As is well known, the class of WOD random variables contains END random variables, NOD random variables, NSD random variables, NA random variables, and independent random variables as special cases. Hence, it is ... See full document

11

Generalized Empirical Likelihood M Testing for Semiparametric Models with Time Series Data

Generalized Empirical Likelihood M Testing for Semiparametric Models with Time Series Data

... smooth semiparametric models with time series ...so-called M tests originally proposed by Newey (1985) (see White 1994 for a review and some applications to parametric models) and commonly used in empirical ... See full document

26

The consistency of estimator under fixed design regression model with NQD errors

The consistency of estimator under fixed design regression model with NQD errors

... models based on linear process ...[] generalized part results of Liang and Jing [] for negatively associated sequences to the case of negatively orthant dependent sequences, and so ... See full document

12

Strong consistency of estimators in partially linear models for longitudinal data with mixing dependent structure

Strong consistency of estimators in partially linear models for longitudinal data with mixing dependent structure

... random errors of the models ...weighted semiparametric least square estimator and derived asymptotic properties of the ...AR(1) errors, Gao and Anh [14] with long-memory errors, Sun et ... See full document

18

Berry Esseen bounds for wavelet estimator in semiparametric regression model with linear process errors

Berry Esseen bounds for wavelet estimator in semiparametric regression model with linear process errors

... For the properties of strong-mixing, one can read the book of Lin and Liu [7]. Recently, Yang and Li [8-10] and Xing et al. [11-13] established moment bounds and maximal moment inequality for partial sums for strong ... See full document

18

A semiparametric regression model for longitudinal data with non stationary errors

A semiparametric regression model for longitudinal data with non stationary errors

... new semiparametric longitudinal mean-covariance model in which the effects on dependent variable of some explanatory variables are linear and others are nonlinear, while the within-subject correlations are ... See full document

29

The consistency for estimator of nonparametric regression model based on NOD errors

The consistency for estimator of nonparametric regression model based on NOD errors

... regression model, Ren and Chen [11] obtained the strong consistency for the least squares estimator of b and the nonparametric estimator of g(t) based on NA samples, Hu [12] obtained the ... See full document

13

Adjustment of the Auxiliary Variable(s) for Estimation of a Finite Population Mean

Adjustment of the Auxiliary Variable(s) for Estimation of a Finite Population Mean

... weighted estimator, whose weights sum up to one, with the aim to obtain more precise ...proposed estimator is compared with the estimators proposed by Chakrabarty (1979), Singh and Singh (1997), Singh ... See full document

29

Consistency of the structured total least squares estimator in a multivariate errors in variables model

Consistency of the structured total least squares estimator in a multivariate errors in variables model

... . This modification simplifies the analysis. In Section 6, we study the properties of the inverse weight matrix − 1 . We establish exponential decay of the elements of − 1 , away from the main diagonal. This property is ... See full document

44

Improvement Over General And Wider Class Of Estimators Using Ranked Set Sampling

Improvement Over General And Wider Class Of Estimators Using Ranked Set Sampling

... resultant estimator cannot be reduced further than that given in ...ratio estimator, product estimator and power transformation estimator are the special cases of the class of estimators ... See full document

5

LQ-moments for statistical analysis of extreme events

LQ-moments for statistical analysis of extreme events

... estimators based on five points quantiles using weighted kernel estimators are introduced for characterizing the upper quantiles of distributions and larger events in a ... See full document

11

A Signal-to-Noise Ratio Estimator for Generalized Linear Model Systems

A Signal-to-Noise Ratio Estimator for Generalized Linear Model Systems

... To illustrate our method for a GLM system we consider two simulated neural spike trains modeled as point processes [1], [15], [3], [23], [24]. A point process is a time-series of 0-1 random events that occur in ... See full document

7

Berry Esseen bounds of weighted kernel estimator for a nonparametric regression model based on linear process errors under a LNQD sequence

Berry Esseen bounds of weighted kernel estimator for a nonparametric regression model based on linear process errors under a LNQD sequence

... bound based on linear process errors under negatively associated ran- dom ...wavelet estimator for a nonparametric regression model with linear process errors generated by ϕ-mixing ... See full document

12

Clinical factors associated with a conservative gait pattern in older male veterans with diabetes

Clinical factors associated with a conservative gait pattern in older male veterans with diabetes

... Patients with the conservative gait pattern had lower walk- ing speed and decreased stride length compared to nor- mal gait. (0.68 m/s v. 0.91 m/s, p < 0.001; 1.04 m v. 1.24 m, p < ... See full document

5

A Generalized Class of Jack-Knifed Estimator for Population Mean Using Two Auxiliary Variables under Measurement Errors

A Generalized Class of Jack-Knifed Estimator for Population Mean Using Two Auxiliary Variables under Measurement Errors

... a generalized class of estimators of population mean, ratio and product of population means using auxiliary information of two variables in presence of measurement ...the generalized estimator in ... See full document

11

A Kinematic Model to Compensate the Structural Deformations in Machine Tools Using Fibre Bragg Grating (FBG) Sensors

A Kinematic Model to Compensate the Structural Deformations in Machine Tools Using Fibre Bragg Grating (FBG) Sensors

... the model for a successful application of the technique. Different model structures have been used to calculate thermal errors such as (MRA) Multiple Regression Analysis [17], (ANN) artificial neural ... See full document

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