In this paper, we introduced a transaction costs function and established a portfolio model of risk management with second stochastic dominance constraints. This model does not need to make any assumptions about the utility function of the investors and the distribution of the risk assets income, and it can ensure that the choices of the risk-averse investor can be randomly better than a reference value, so it can avoid the high risk investment. We provide a smoothing penalty sample average approximation method for solving this optimization problem. We prove that the smoothing penalty problem is equivalent to the original problem. Numerical results prove that the model and the method are efficient.
A Markov observation model with dividend is defined and the interpretation of the practical significance is given. We try to use an irreducible and homogeneous discrete-time Markov chain to modulate the inter-observation times and embed a dividend strategy. In the Markov observation model with dividend, a system of liner equations for the expected discounted value of dividends until ruin time is derived. Moreover, an explicit expression is obtained and proved. Finally, some interesting properties are illustrated by numerical analysis and by comparing with the complete compound binomial model with dividend.
In this paper, the complete moment convergence for weighted sums of -mixing random variable series are investigated. By using Rosenthal type inequality, we obtain complete moment convergence theorems for weighted sums of -mixing random variable series, which generalize and improve the corresponding results.
This paper investigates the investment-dividend
optimization problem for a corporation with transaction costs and investment
constraints. The main feature is that we assume general constraints on
investments including the special case of short-sale and borrowing constraints.
This results in a regular-impulse stochastic control problem. The nontrivial
case is that the investment can't meet the loss of wealth due to discounting.
In this case, delicate analysis is carried out on QVI w.r.t. three possible
situations, leading to an explicit construction of the value functions
together with the optimal policies. We also give explicit conclusion of the
trivial case at last.
This paper studies estimation in functional partial
linear composite quantile regression model in which the dependent variable
is related to both a function-valued random variable in linear form and a
real-valued random variable in nonparametric form. The functional principal
component analysis and regression splines are employed to estimate the slope
function and the nonparametric function respectively, and the convergence
rates of the estimators are obtained under some regularity conditions.
Simulation studies and a real data example are presented for illustration
of the performance of the proposed estimators.
The hybrid censoring scheme is a mixture of type-I and
type-II censoring schemes. It is a popular censoring scheme in the literature
of life data analysis. Mixed exponential distribution (MED) models is a class
of favorable models in reliability statistics. Nevertheless, there is no much
discussion to focus on parameters estimation for MED models with hybrid
censored samples. We will address this problem in this paper. The EM
(Expectation-Maximization) algorithm is employed to derive the closed form of
the maximum likelihood estimators (MLEs). Finally, Monte Carlo simulations and
a real-world data analysis are conducted to illustrate the proposed method.
It is difficult to get an accurate optimum design when
the experimental design area is very irregular under complex constraints. This
paper constructs a random search algorithm for mixture experiments designed
(MDRS). Firstly, generating an initial points set in areas with complex constraints
by the Monte-Carlo method, then use MDRS algorithm iterative to approximate
optimum set. By way of example verification, this method is effective. It can
be used as a standard measure of other designs, that is the only effective when
given superior to other designs approximate optimal solution.