Asymptotic behavior of Mean-CVaR portfolio selection model under nonparametric framework

Portfolio selection is an important issue in finance and it involves the balance between risk and return. This paper investigates portfolio selection under Mean-CVa R model in a nonparametric framework with α-mixing data as financial data tends to be dependent. Many works have provided some insight into the performance of portfolio selection from the aspects of data and simulation while in this paper we concentrate on the asymptotic behaviors of the optimal solutions and risk estimation in theory.

A KIND OF URN MODEL FOR ADAPTIVE SEQUENTIAL DESIGN

This paper proposes a new kind of generalized Friendman's urn model,which with adaptive nonhomogeneous generating matrix.This model may be applied in sequential medical experiment.In this model some limit theorems (strong consistency and asymptot- ical normality) have been obtained.

Asymptotically efficient parameter estimation for ordinary differential equations

Parameter estimation for ordinary differential equations arises in many fields of science and engineering. To be the best of our knowledge, traditional methods are often either computationally intensive or inaccurate for statistical inference. Ramsay et al.(2007) proposed a generalized profiling procedure. It is easily implementable and has been demonstrated to have encouraging numerical performance. However, little is known about statistical properties of this procedure. In this paper, we provide a theoretical justification of the generalized profiling procedure. Under some regularity conditions, the procedure is shown to be consistent for a broad range of tuning parameters. When the tuning parameters are sufficiently large, the procedure can be further shown to be asymptotically normal and efficient.

On thermodynamic self-consistency of generic axiomatic-nonextensive statistics

Generic axiomatic-nonextensive statistics introduces two asymptotic properties,to each of which a scaling function is assigned.The first and second scaling properties are characterized by the exponents c and d,respectively.In the thermodynamic limit,a grand-canonical ensemble can be formulated.The thermodynamic properties of a relativistic ideal gas of hadron resonances are studied,analytically.It is found that this generic statistics satisfies the requirements of the equilibrium thermodynamics.Essential aspects of the thermodynamic self-consistency are clarified.Analytical expressions are proposed for the statistical fits of various transverse momentum distributions measured in most-central collisions at different collision energies and colliding systems.Estimations for the freezeout temperature（T_（ch）） and the baryon chemical potential（μ_b） and the exponents c and d are determined.The earlier are found compatible with the parameters deduced from Boltzmann-Gibbs（BG） statistics（extensive）,while the latter refer to generic nonextensivities.The resulting equivalence class（c,d） is associated with stretched exponentials,where Lambert function reaches its asymptotic stability.In some measurements,the resulting nonextensive entropy is linearly composed on extensive entropies.Apart from power-scaling,the particle ratios and yields are excellent quantities to highlighting whether the particle production takes place（non）extensively.Various particle ratios and yields measured by the STAR experiment in central collisions at 200,62.4 and 7.7 GeV are fitted with this novel approach.We found that both c and d 〈 1,i.e.referring to neither BG-nor Tsallis-type statistics,but to（c,d）-entropy,where Lambert functions exponentially rise.The freezeout temperature and baryon chemical potential are found comparable with the ones deduced from BG statistics（extensive）.We conclude that the particle production at STAR energies is likely a nonextensive process but not necessarily BG or Tsallis type.

An Efficient Multiple Imputation Approach for Estimating Equations with Response Missing at Random and High-Dimensional Covariates

Empirical-likelihood-based inference for parameters defined by the general estimating equations of Qin and Lawless(1994) remains an active research topic. When the response is missing at random(MAR) and the dimension of covariate is not low, the authors propose a two-stage estimation procedure by using the dimension-reduced kernel estimators in conjunction with an unbiased estimating function based on augmented inverse probability weighting and multiple imputation(AIPW-MI) methods. The authors show that the resulting estimator achieves consistency and asymptotic normality. In addition, the corresponding empirical likelihood ratio statistics asymptotically follow central chi-square distributions when evaluated at the true parameter. The finite-sample performance of the proposed estimator is studied through simulation, and an application to HIV-CD4 data set is also presented.

Composite quantile regression estimation for P-GARCH processes

We consider the periodic generalized autoregressive conditional heteroskedasticity(P-GARCH) process and propose a robust estimator by composite quantile regression. We study some useful properties about the P-GARCH model. Under some mild conditions, we establish the asymptotic results of proposed estimator.The Monte Carlo simulation is presented to assess the performance of proposed estimator. Numerical study results show that our proposed estimation outperforms other existing methods for heavy tailed distributions.The proposed methodology is also illustrated by Va R on stock price data.