A time-varying coefficient-based manipulability-maximizing scheme for motion control of redundant robots subject to varying joint-velocity limits

2012 ◽  
Vol 34 (2) ◽  
pp. 202-215 ◽  
Author(s):  
Yunong Zhang ◽  
Jun Li ◽  
Zhijun Zhang
Keyword(s):  
Motion Control ◽  
Time Varying ◽  
Varying Coefficient ◽  
Redundant Robots ◽  
Joint Velocity

scholarly journals Estimating latent group structure in time-varying coefficient panel data models

2019 ◽  
Author(s):  
Jia Chen
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Clustering Method ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Latent Group ◽  
Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.

A NOTE ON MUTH'S RATIONAL EXPECTATIONS HYPOTHESIS: A TIME-VARYING COEFFICIENT INTERPRETATION

2006 ◽  
Vol 10 (3) ◽  
pp. 415-425 ◽  
Author(s):  
P.A.V.B. SWAMY ◽  
GEORGE S. TAVLAS
Keyword(s):  
Rational Expectations ◽  
Econometric Model ◽  
Objective Probability ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Exact Representation ◽  
Expectations Hypothesis ◽  
Varying Coefficient ◽  
True Model ◽  
Necessary And Sufficient

Under certain interpretations of its coefficients, a specified econometric model is an exact representation of the “true” model, defining the “objective” probability distribution. This note enumerates these interpretations. In the absence of the conditions implied by these interpretations, the econometric model is misspecified. The note shows that model misspecifications prevent the satisfaction of a necessary and sufficient condition for individual expectations to be rational in Muth's sense. Whereas restrictive forms of econometric models can give very inaccurate predictions, this note describes the conditions under which the predictions generated from time-varying coefficient models coincide with the predictions generated from the relevant economic theory.

Trajectory planning for coordinately operating robots

1990 ◽  
Vol 4 (3) ◽  
pp. 165-177 ◽  
Author(s):  
Y. P. Chien ◽  
Qing Xue
Keyword(s):  
Trajectory Planning ◽  
Three Dimensional ◽  
Initial Position ◽  
Minimum Time ◽  
Future Research ◽  
Rigid Object ◽  
Redundant Robots ◽  
Planning Algorithm ◽  
The Common ◽  
Joint Velocity

An efficient locally minimum-time trajectory planning algorithm for coordinately operating multiple robots is introduced. The task of the robots is to carry a common rigid object from an initial position to a final position along a given path in three-dimensional workspace in minimum time. The number of robots in the system is arbitrary. In the proposed algorithm, the desired motion of the common object carried by the robots is used as the key to planning of the trajectories of all the non-redundant robots involved. The search method is used in the trajectory planning. The planned robot trajectories satisfy the joint velocity, acceleration and torque constraints as well as the path constraints. The other constraints such as collision-free constraints, can be easily incorporated into the trajectory planning in future research.

scholarly journals Changing effects of monetary policy in the US–evidence from a time-varying coefficient VAR

2008 ◽  
Vol 40 (18) ◽  
pp. 2353-2360 ◽  
Author(s):  
Florian Höppner ◽  
Christian Melzer ◽  
Thorsten Neumann
Keyword(s):  
Monetary Policy ◽  
Time Varying ◽  
Varying Coefficient ◽  
The Us

scholarly journals TIME-VARYING COEFFICIENT MODELS: A PROPOSAL FOR SELECTING THE COEFFICIENT DRIVER SETS

2016 ◽  
Vol 21 (5) ◽  
pp. 1158-1174 ◽  
Author(s):  
Stephen G. Hall ◽  
P. A. V. B. Swamy ◽  
George S. Tavlas
Keyword(s):  
Arbitrary Element ◽  
The Other ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Varying Coefficient ◽  
Sovereign Bond ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Bond Spreads

Coefficient drivers are observable variables that feed into time-varying coefficients (TVCs) and explain at least part of their movement. To implement the TVC approach, the drivers are split into two subsets, one of which is correlated with the bias-free coefficient that we want to estimate and the other with the misspecification in the model. This split, however, can appear to be arbitrary. We provide a way of splitting the drivers that takes account of any nonlinearity that may be present in the data, with the aim of removing the arbitrary element in driver selection. We also provide an example of the practical use of our method by applying it to modeling the effect of ratings on sovereign-bond spreads.

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