... general **least** **squares** ...likelihood **estimation** (also in a frequentist framework) can be established with little difficulty from the results presented ...the **estimation** of the unknown ...

31

... numbers. **Least** **squares** **estimation** is derived, and the asymptotic distribution of the proposed estimator is ...This **estimation** procedure is well deﬁned because if we use crisp data instead of ...

10

... We have concentrated on the convergence and the rate of convergence of empirical processes of residuals from nonlinear **least** **squares** **estimation** problems. These processes can be used to answer many ...

22

... nonlinear **least**-**squares** **estimation** to obtain row and column factors for splitting trip totals from and to larger geographical areas into smaller ...

22

... In recent years, a large part of the time series and econometric literature was devoted to weaken the strong noise assumption. In particular, Romano and Thombs (1996) showed that the significance limits of the sample ...

44

... parameter **estimation** in the Bass model by nonlinear **least** **squares** fitting the adoption ...weighted **least** **squares** **estimation** of a three-parameter Weibull density with a ...

10

... parameter **estimation** in the framework of a **least** **squares** **estimation** problem ([10] and all the references ...the **least** **squares** ...appropriate **estimation** technique varies; ...

16

... tributed **estimation** problems of type ...state **estimation** in power sys- tems; therein, a phasorial representation of voltages and currents is usually utilized, wherein non-linearity in gen- eral emerges from ...

15

... GARCH (1,1) models are widely used for modelling processes with time varying volatility. These include financial time series, which can be particularly heavy tailed. In this paper, we propose a log- transform-based ...

41

... All three GLS estimators are typically rather close together in terms of RMSE and MAE. GLS2 and GLS3 results are in fact almost indistinguishable in the ﬁgures for all DGPs and sample sizes. Thus, reestimating α in Step ...

22

... approached **estimation** of joint production function through ...and **estimation** of a general implicit production ...of **estimation** of multiple/joint production functions as an exercise in ...

13

... simple nite dierence scheme, containing ux balance conditions over conductivity discontinu- ities, is used to approximate the ow equation. Piecewise constant functions are used for the conductivities, and piecewise ...

7

... parameter **estimation** step is performed using various types LLS **estimation** algorithms because of the excellent characteristics where it is stable and efficient in terms of numerical computation ...

7

... In the previous subsection, the shift is ignored and the order determination is based on a misspecified model which leads to over **estimation**. Thus if a high order is identified in practice, one should consider a ...

31

... have already proved the strong consistency and asymptotic normality of the LSE of (a, b) based on continuous time observations (Yt) t ∈[0 ,T ] , T > 0, in case of a subcritical CIR pr[r] ...

22

... the **estimation** problem for a class of dynamic games of incomplete information that generalizes the single agent discrete Markov decision models surveyed in Rust (1994); for a recent survey see Aguirregabiria and ...

33

... is **estimation** of the subsurface physical pa- rameters, particularly the hydraulic ...a **least** **squares** **estimation** procedure for determining groundwater velocities from limited ...

15

... In this paper we propose a new methodology to estimate derivatives in nonparametric re- gression. The method includes two main steps: construct a sequence of symmetric difference quotients, and estimate the derivative ...

25

... optimal **estimation** of a linear quan- tum system variables given the non-commutative outputs within the framework of non- commutative probability ...a **least** **squares** error ...

25

... the **least**-**squares** me- thod (for linear systems) which results as a consequence of the analysis that the mean observational error will fall within certain given ...the **least**-**squares** ...

7