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As the economic and financial characteristics of countries change, so would be their betas and correlations of their investment returns with that of the U.S. Such changes are expected to be particularly significant for emerging market nations as they strive for rapid industrialization and modernization. OLS estimator for the beta coefficient would not be the Best Linear Unbiased Estimator (BLUE) if beta is non‐stationary or changes from period to period. This paper proposes a special type of time weighted least square method (TWLS), which assigns greater weights on the regression errors in more recent periods, for estimating the current beta. This TWLS approach can tackle the problem of intertemporal heteroscedasticity and thus yields a beta that is more efficient. The breakthrough lies on the viability of the method without a‐priori knowledge or estimation of the values of the weights. This yields a significant practical advantage since the weights are unobservable in the real world. Since the Time Weighted Method estimator is the coefficient estimator of beta value for the latest period in the sample, statisticians who base their forecasts on the beta estimates derived from the Time Weighted Least Square can expect to outperform those relying on beta values obtained from conventional estimation. We use a sample of daily returns of thirty‐one emerging markets stock over the period of January 1, 2000 through December 31, 2002. We find that most of the tstatistics for the variances are significant at the 95 per cent level, indicating that the Var(s)’s are not zero for nearly every emerging‐markets. This implies that the betas for these markets do shift over time.

The purpose of this paper is to present a new methodology for identification of the parameters of the local linear Takagi‐Sugeno fuzzy models using weighted recursive least squares. The weighted recursive least squares (WRLS) is sensitive to initialization which leads to no converge. In order to overcome this problem, Euclidean particle swarm optimization (EPSO) is employed to optimize the initial states of WRLS. Finally, validation results are given to demonstrate the effectiveness and accuracy of the proposed algorithm. A comparative study is presented. Validation results involving simulations of numerical examples and the liquid level process have demonstrated the practicality of the algorithm.

A new method for nonlinear system modelling. The proposed algorithm is employed to optimize the initial states of WRLS algorithm in two phases of learning algorithm.

The results obtained using this novel approach were comparable with other modeling approaches reported in the literature. The proposed algorithm is able to handle various types of modeling problems with high accuracy.

In this paper, a new method is employed to optimize the initial states of WRLS algorithm in two phases of the learning algorithm.

We extend Vuong’s (1989) model-selection statistic to allow for complex survey samples. As a further extension, we use an M-estimation setting so that the tests apply to general estimation problems – such as linear and nonlinear least squares, Poisson regression and fractional response models, to name just a few – and not only to maximum likelihood settings. With stratified sampling, we show how the difference in objective functions should be weighted in order to obtain a suitable test statistic. Interestingly, the weights are needed in computing the model-selection statistic even in cases where stratification is appropriately exogenous, in which case the usual unweighted estimators for the parameters are consistent. With cluster samples and panel data, we show how to combine the weighted objective function with a cluster-robust variance estimator in order to expand the scope of the model-selection tests. A small simulation study shows that the weighted test is promising.

The Weighted Linear Least Squares (WLLS) problem has many different applications in sciences and engineering. The purpose of this paper is to introduce an iterative scheme for solving the WLLS problem.

By considering the splitting techniques in conjunction with Generalized Accelerated Over-relaxation (GAOR) method the authors design a new iterative method to solve the weighted linear least squares problem. Furthermore, within the computational framework, some models of iterative schemes candidates are investigated and evaluated.

In this paper, the authors propose an efficient iterative scheme for solving the WLLS problem. The proposed scheme presented promising results from the aspects of both convergence behavior and performance. Moreover, comparative results for the proposed schemes are also presented.

Comparison between the new methods and other similar methods for the studied problem shows a remarkable agreement and reveals that the new model is much better in comparison with the existing methods in point of view rate of convergence and computing efficiency, as illustrated by the theoretical analysis and numerical results presented.

For solving WLLS more attention has recently been paid on a special class of splitting techniques with the preconditioned GAOR method. In this paper, the authors use a different splitting for the GAOR method and present a promising class of methods. The convergence results of the iterative algorithm are also proposed. Several examples are given to show the efficiency of the presented methods.

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there is also uncertainty as to which method one should deploy, prompting model averaging over user-defined choices. Specifically, we propose, and detail, a nonparametric regression estimator averaged over choice of kernel, bandwidth selection mechanism and local-polynomial order. Simulations and an empirical application are provided to highlight the potential benefits of the method.

The purpose of this paper is to propose a new joint friction model, which can accurately model the real friction, especially in cases with sudden changes in the motion direction. The identification and sensor-less control algorithm are investigated to verify the validity of this model.

The proposed friction model is nonlinear and it considers the angular displacement and angular velocity of the joint as a secondary compensation for identification. In the present study, the authors design a pipeline – including a manually designed excitation trajectory, a weighted least squares algorithm for identifying the dynamic parameters and a hand guiding controller for the arm’s direct teaching.

Compared with the conventional joint friction model, the proposed method can effectively predict friction factors during the dynamic motion of the arm. Then friction parameters are quantitatively obtained and compared with the proposed friction model and the conventional friction model indirectly. It is found that the average root mean square error of predicted six joints in the proposed method decreases by more than 54%. The arm’s force control with the full torque using the estimated dynamic parameters is qualitatively studied. It is concluded that a light-weight industrial robot can be dragged smoothly by the hand guiding.

In the present study, a systematic pipeline is proposed for identifying and controlling an industrial arm. The whole procedure has been verified in a commercial six DOF industrial arm. Based on the conducted experiment, it is found that the proposed approach is more accurate in comparison with conventional methods. A hand-guiding demo also illustrates that the proposed approach can provide the industrial arm with the full torque compensation. This essential functionality is widely required in many industrial arms such as kinaesthetic teaching.

First, a new friction model is proposed. Based on this model, identifying the dynamic parameter is carried out to obtain a set of model parameters of an industrial arm. Finally, a smooth hand guiding control is demonstrated based on the proposed dynamic model.

There is a growing literature in nonparametric econometrics in the recent two decades. Given the space limitation, it is impossible to survey all the important recent developments in nonparametric econometrics. Therefore, we choose to limit our focus on the following areas. In Section 2, we review the recent developments of nonparametric estimation and testing of regression functions with mixed discrete and continuous covariates. We discuss nonparametric estimation and testing of econometric models for nonstationary data in Section 3. Section 4 is devoted to surveying the literature of nonparametric instrumental variable (IV) models. We review nonparametric estimation of quantile regression models in Section 5. In Sections 2–5, we also point out some open research problems, which might be useful for graduate students to review the important research papers in this field and to search for their own research interests, particularly dissertation topics for doctoral students. Finally, in Section 6 we highlight some important research areas that are not covered in this paper due to space limitation. We plan to write a separate survey paper to discuss some of the omitted topics.

Over the past 20 years, partial least squares (PLS) has become a popular method in marketing research. At the same time, several methodological studies have demonstrated problems with the technique but have had little impact on its use in marketing research practice. This study aims to present some of these criticisms in a reader-friendly way for non-methodologists.

Key critiques of PLS are summarized and demonstrated using existing data sets in easily replicated ways. Recommendations are made for assessing whether PLS is a useful method for a given research problem.

PLS is fundamentally just a way of constructing scale scores for regression. PLS provides no clear benefits for marketing researchers and has disadvantages that are features of the original design and cannot be solved within the PLS framework itself. Unweighted sums of item scores provide a more robust way of creating scale scores.

The findings strongly suggest that researchers abandon the use of PLS in typical marketing studies.

This paper provides concrete examples and techniques to practicing marketing and social science researchers regarding how to incorporate composites into their work, and how to make decisions regarding such.

This work presents a novel perspective on PLS critiques by showing how researchers can use their own data to assess whether PLS (or another composite method) can provide any advantage over simple sum scores. A composite equivalence index is introduced for this purpose.

The purpose of this paper is to present extended least squares support vector machines (LS‐SVM) where data selection methods are used to get sparse LS‐SVM solution, and to overview and compare the most important data selection approaches.

The selection methods are compared based on their theoretical background and using extensive simulations.

The paper shows that partial reduction is an efficient way of getting a reduced complexity sparse LS‐SVM solution, while partial reduction exploits full knowledge contained in the whole training data set. It also shows that the reduction technique based on reduced row echelon form (RREF) of the kernel matrix is superior when compared to other data selection approaches.

Data selection for getting a sparse LS‐SVM solution can be done in the different representations of the training data: in the input space, in the intermediate feature space, and in the kernel space. Selection in the kernel space can be obtained by finding an approximate basis of the kernel matrix.

The RREF‐based method is a data selection approach with a favorable property: there is a trade‐off tolerance parameter that can be used for balancing complexity and accuracy.

The paper gives contributions to the construction of high‐performance and moderate complexity LS‐SVMs.