In many sports contests, the equilibrium requires players to randomize across repeated rounds, i.e., exhibit no temporal predictability. Such sports data present a window into the (in)efficiency of random sequence generation in a natural competitive environment, where the decision makers (tennis players) are both highly experienced and incentivized compared to laboratory studies. I resolve a long-standing debate about whether professional players’ tennis serve directions are serially independent (Hsu, Huang & Tang, 2007) or not (Walker & Wooders, 2001) using a new dataset that is two orders of magnitude larger than those studies. I examine both between- and within-player determinants of the degree of serial (in)dependence. Evidence of the existence of significant serial dependence across serves is presented, even among players ranked Number 1 in the world. Furthermore, significant heterogeneity was found with respect to the strength of serial dependence and also its sign. A novel finding is that Number 1 and Number 2 ranked players tend to under-alternate on average, whereas in line with previous findings, the lower-ranked the players, the greater their tendency to over-alternate. Within-player analyses show that high-ranked players do not condition their randomization behavior on their opponent’s ranking. However, the under-alternation of top players would be consistent with a best-response to beliefs that the population of opponents over-alternates on average. Finally, the degree of observed serial dependence is not systematically related to other match variables proxying for match difficulty, fatigue, and psychological pressure.

This paper is concerned with the dynamic merit of robot manipulators and method for the optimization of manipulator dynamics in the case of an extremely bad design, which can be put into three categories: 1. Acceleration performance: minimize 2. Velocity performance: minimize 3. Comprehensive performance: minimize , and are the dynamics indices of API, VPI and CD PI under various conditions respectively.

The exchange algorithm is presented and a procedure is developed to solve this kind of Minimax problem. An example is discussed to illustrate the application of the optimization method. The large uniform magnitude of a joint relative driving torque provided by this dynamics optimization procedure will be made into a large work space and large torques.

In a decision problem with uncertainty a decision maker receives partial information about the actual state via an information structure. After receiving a signal, he is allowed to withdraw and gets zero profit. We say that one structure is better than another when a withdrawal option exists if it may never happen that one structure guarantees a positive profit while the other structure guarantees only zero profit. This order between information structures is characterized in terms that are different from those used by Blackwell's comparison of experiments. We also treat the case of a malevolent nature that chooses a state in an adverse manner. It turns out that Blackwell's classical characterization also holds in this case.

One of the popular techniques for solving GPS pseudorange nonlinear quadratic equations to obtain the receiver's position and clock bias involves linearizing the equations and solving them by the least-squares (LS) scheme, which is based on the L2 minimization criterion. When outliers are a problem, LS estimation scheme may not be the best approach because the LS estimator minimizes the mean squared error of the observations. This paper discusses the implementation aspects of L1 and L∞ criteria, and their outlier resistance performance for GPS positioning. Three ordinary differential equation formulation schemes and corresponding circuits of neuron-like analogue L1 (least-absolute), L∞ (minimax), and L2 (least-squares) processors will be employed for GPS navigation processing. The circuits of simple neuron-like analogue processors are employed essentially for solving systems of linear equations. Experiments on single epoch and thereafter kinematic positioning will be conducted by computer simulation for investigating the outlier resistance performance for the least-absolute and minimax schemes as compared to the one provided by the least-squares scheme.

We prove that, for every bounded and measurable forcing $p(t)$, the differential equation $\ddot{u}+u^{1/3} =p(t)$ has bounded solutions with arbitrarily large amplitude. In general it is not possible to say that all solutions are bounded, as shown by an example due to Littlewood.

The proof is based on a variational method which can be seen as a dual version of Nehari's method for boundary value problems on compact intervals.

We study estimation of the tail-decay parameter of the marginal distribution corresponding to a discrete-time, real-valued stationary stochastic process. Assuming that the underlying process is short-range dependent, we investigate properties of estimators of the tail-decay parameter which are based on the maximal extreme value of the process observed over a sampled time interval. These estimators only assume that the tail of the marginal distribution is roughly exponential, plus some modest ‘mixing’ conditions. Consistency properties of these estimators are established, as well as minimax convergence rates. We also provide some discussion on estimating the pre-exponent, when a more refined tail asymptotic is assumed. Properties of a certain moving-average variant of the extremal-based estimator are investigated as well. In passing, we also characterize the precise dependence (mixing) assumptions that support almost-sure limit theory for normalized extreme values and related first-passage times in stationary sequences.

Assume that we want to estimate – σ, the abscissa of convergence of the Laplace transform. We show that no non-parametric estimator of σ can converge at a faster rate than (log n)–1, where n is the sample size. An optimal convergence rate is achieved by an estimator of the form where xn = O(log n) and is the mean of the sample values overshooting xn. Under further parametric restrictions this (log n)–1 phenomenon is also illustrated by a weak convergence result.