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It is known that adaptive estimation models are used in different time intervals of the motion projection of maneuvering targets. In this study Interacting Multiple Model (IMM) estimation technique is implemented and its performance is tested on an air traffic control track simulation. IMM algorithm is a second degree Bayesian estimation technique and an adaptive estimation model. Air traffic control entity motion is initially simulated with a constant speed of 125 m/s motion for 100 seconds then turned to the left with a 30 ° with a 3 m/s angular speed for 30 seconds and finally finished its motion with a constant speed of 125 m/s for 70 seconds. Then a sensor is placed on a specific coordinate to measure the trajectory motion of the air traffic entity. For the measurements and process simulated Gaussian noise is added during the calculations. The simulated air traffic control entity's motion trajectory and the measurements of the sensor are initially modelled with Interacting Multiple Model-Linear (IMM-L) technique, then Interacting Multiple Model-Coordinated Turn (IMM-CT) and finally they are modelled with a Kalman filter. According to the results the best estimate matches of the motion trajectory of the air traffic control entity is generated by IMM-CT, then Kalman Filter and finally IMM-L algorithms subsequently. [ABSTRACT FROM AUTHOR]

A new estimation concept for semiparametric regression models based on smoothing splines has been introduced in Eubank et al. (1998). The two different approaches based on smoothing splines have been compared by Schimek (2000) according to this estimation concept. In this study, the estimation of the semiparametric regression model is performed with two different approaches called partial splines of Green and Silverman and Speckman's approach and compared with the estimations of the ordinary least squares. The smoothing parameter is selected via generalized cross-validation in smoothing splines and inferences have been discussed for both the parametric and the nonparametric part of the model. For this purpose an application is performed by using a program coded in MATLAB. [ABSTRACT FROM AUTHOR]

Restricted maximum likelihood (REML) estimation of the parameters of the mixed model has become commonplace, even becoming the default option in many statistical software packages. However, a review of the literature indicates a need to update and clarify model selection techniques under REML, as ambiguities exist on the appropriateness of existing information criteria in this setting. A simulation study as well as an application assisted in gaining an understanding of the performance of information criteria in selecting the best model when using REML estimation. [ABSTRACT FROM AUTHOR]