Parameter estimation and model testing for Markov processes via conditional characteristic functions

Markov processes are used in a wide range of disciplines, including finance. The transition densities of these processes are often unknown. However, the conditional characteristic functions are more likely to be available, especially for L\'{e}vy-driven processes. We propose an empirical likelihood approach, for both parameter estimation and model specification testing, based on the conditional characteristic function for processes with either continuous or discontinuous sample paths. Theoretical properties of the empirical likelihood estimator for parameters and a smoothed empirical likelihood ratio test for a parametric specification of the process are provided. Simulations and empirical case studies are carried out to confirm the effectiveness of the proposed estimator and test.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ400 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Goodness-of-fit tests for a heavy tailed distribution

For testing whether a distribution function is heavy tailed, we study the Kolmogorov test, Berk-Jones test, score test and their integrated versions. A comparison is conducted via Bahadur efficiency and simulations. The score test and the integrated score test show the best performance. Although the Berk-Jones test is more powerful than the Kolmogorov-Smirnov test, this does not hold true for their integrated versions; this differs from results in \\citet{EinmahlMckeague2003}, which shows the difference of Berk-Jones test in testing distributions and tails

Topological Gauge Structure and Phase Diagram for Weakly Doped Antiferromagnets

We show that the topological gauge structure in the phase string theory of the {\rm t-J} model gives rise to a global phase diagram of antiferromagnetic (AF) and superconducting (SC) phases in a weakly doped regime. Dual confinement and deconfinement of holons and spinons play essential roles here, with a quantum critical point at a doping concentration $x_c\simeq 0.043$. The complex experimental phase diagram at low doping is well described within such a framework.Comment: 4 pages, 2 figures, modified version, to appear in Phys. Rev. Let

Study on the Rough-set-based Clustering Algorithm for Sensor Networks

The traditional clustering algorithm is a very typical level routing algorithm in wireless sensor networks (WSN). On the basis of the classical LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm, this paper proposes an energy efficient clustering algorithm in WSN. Through the introduction of rough set, the new algorithm mainly introduces how to confirm an optimized strategy to choose the cluster head effectively by the simplified decision table. That is to say, by discrete normalized data preprocessing of attribute value, getting discretization decision table. Finally, the results from simulated experiments show that the clustering algorithm based on rough set theory can optimize the clustering algorithm in network data. That is to say, the rough-set-based clustering algorithm can effectively choose the cluster head, balance the energy of the nodes in the cluster and prolong the lifetime of sensor networks

Convolutions of heavy-tailed random variables and applications to portfolio diversification and MA(1) time series

The paper characterizes first and second order tail behavior of convolutions of i.i.d. heavy tailed random variables with support on the real line. The result is applied to the problem of risk diversification in portfolio analysis and to the estimation of the parameter in a MA(1) model

Photon Momentum Transfer in Single-Photon Double Ionization of Helium

We theoretically and experimentally investigate the photon momentum transfer in single-photon double ionization of helium at various large photon energies. We find that the forward shifts of the momenta along the light propagation of the two photoelectrons are roughly proportional to their fraction of the excess energy. The mean value of the forward momentum is about 8/5 of the electron energy divided by the speed of light. This holds for fast and slow electrons despite the fact that the energy sharing is highly asymmetric and the slow electron is known to be ejected by secondary processes of shake off and knockout rather than directly taking its energy from the photon. The biggest deviations from this rule are found for the region of equal energy sharing where the quasifree mechanism dominates double ionization

Higher moment singularities explored by the net proton non-statistical fluctuations

We use the non-statistical fluctuation instead of the full one to explore the higher moment singularities of net proton event distributions in the relativistic Au+Au collisions at $\sqrt{s_{NN}}$ from 11.5 to 200 GeV calculated by the parton and hadron cascade model PACIAE. The PACIAE results of mean ($M$), variance ($\sigma^2$), skewness ($S$), and kurtosis ($\kappa$) are consistent with the corresponding STAR data. Non-statistical moments are calculated as the difference between the moments derived from real events and the ones from mixed events, which are constructed by combining particles randomly selected from different real events. An evidence of singularity at $\sqrt{s_{NN}}\sim$ 60 GeV is first seen in the energy dependent non-statistical $S$ and $S\sigma$.Comment: 5 pages,5 figure

A bootstrap-based method to achieve optimality on estimating the extreme-value index

Estimators of the extreme-value index are based on a set of upper order statistics. We present an adaptive method to choose the number of order statistics involved in an optimal way, balancing variance and bias components. Recently this has been achieved for the similar but somewhat less involved case of regularly varying tails (Drees and Kaufmann(1997); Danielsson et al.(1996)). The present paper follows the line of proof of the last mentioned paper