医院综合效益评价中的权数(三)——估计权数

在医院综合效益评价中,估计权数是由评价专家根据经验对评价指标的重要程度的一种评估,这类数据通常用AHP法进行处理.本文用AHP的递推算法计算估计权数,这种算法不仅计算简单,而且易达到一致性的要求,其估计的剩余误差小于AHP法.

EDLC charging performance for microgrid applications

In order to review storage performance of the electric double layer capacitor （EDLC） in microgrid applications, charging time and storage efficiency issues are mainly studied aiming at three different charging modes, including the constant voltage charging mode （CVCM）, the constant current charging mode （CCCM） and the constant power charging mode （CPCM）, based on the practical EDLC product. Numerical calculation methods are presented for different charging modes, and the charging efficiency is also reviewed with strict mathematical deductions, which is validated to be accurate enough and applicable through a simple case with the PV/EDLC system illustration. Finally, trade-off problems between charging time and energy loss are also studied. Research results show that the CPCM is more suitable for microgrid networks compared with the traditional constant-voltage and constant-current charging modes. The hybrid charging method is recommended to save energy and keep high efficiency relatively at the same time. However, how to manage the combination percentage of different charging modes in a reasonable way should be dealt with according to the practical requirements.

Parameter estimation methods in generalized weighted functional mean combining forecasting model

A kind of combining forecasting model based on the generalized weighted functional mean is proposed. Two kinds of parameter estimation methods with its weighting coefficients using the algorithm of quadratic programming are given. The efficiencies of this combining forecasting model and the comparison of the two kinds of parameter estimation methods are demonstrated with an example. A conclusion is obtained, which is useful for the correct application of the above methods.

Robust range-parameterized cubature Kalman filter for bearings-only tracking

In order to improve tracking accuracy when initial estimate is inaccurate or outliers exist,a bearings-only tracking approach called the robust range-parameterized cubature Kalman filter(RRPCKF)was proposed.Firstly,the robust extremal rule based on the pollution distribution was introduced to the cubature Kalman filter(CKF)framework.The improved Turkey weight function was subsequently constructed to identify the outliers whose weights were reduced by establishing equivalent innovation covariance matrix in the CKF.Furthermore,the improved range-parameterize(RP)strategy which divides the filter into some weighted robust CKFs each with a different initial estimate was utilized to solve the fuzzy initial estimation problem efficiently.Simulations show that the result of the RRPCKF is more accurate and more robust whether outliers exist or not,whereas that of the conventional algorithms becomes distorted seriously when outliers appear.

Nonparametric estimation of employee stock options

We proposed a new model to price employee stock options （ESOs）. The model is based on nonparametric statistical methods with market data. It incorporates the kernel estimator and employs a three-step method to modify Black- Scholes formula. The model overcomes the limits of Black-Scholes formula in handling option prices with varied volatility. It disposes the effects of ESOs self-characteristics such as non-tradability, the longer term for expiration, the eady exercise feature, the restriction on shorting selling and the employee＇s risk aversion on risk neutral pricing condition, and can be applied to ESOs valuation with the explanatory variable in no matter the certainty case or random case.

Estimation of Money Demand Function of South Korea Considering Regime Switching

The stability of money demand function is an important issue in macroeconomic policy implementation. Money demand of Korean economy was estimated. Cointegration test with time dummy variables results show that there is not only long-run equilibrium relationship between money demand and macroeconomic variables, but also structural breaks in this equilibrium relationships. Least squares, state-space, and Marcov switching methods show that there also has been instability （or regime shifts） of parameters in money demand, especially over 1997 crisis and the early 2000s. This fact implies that monetary policy for stabilization might encounter big problems due to change （instability） of money demand.

Multiple weighted estimates for commutators of multilinear fractional integral operators

Multilinear commutators and iterated commutators of multilinear fractional integral operators with BMO functions are studied. Both strong type and weak type endpoint weighted estimates involving the multiple weights for such operators are established and the weak type endpoint results are sharp in some senses. In particular, we extend the results given by Cruz-Uribe and Fiorenza in 2003 and 2007 to the multilinear setting. Moreover, we modify the weak type of endpoint weighted estimates and improve the strong type of weighted norm inequalities on the multilinear commutators given by Chen and Xue in 2010 and 2011.

Some nonlinear elliptic systems with right-hand side integrable data with respect to the distance to the boundary

In this paper,we study the very weak solutions to some nonlinear elliptic systems with righthand side integrable data with respect to the distance to the boundary.Firstly,we study the existence of the approximate solutions.Secondly,a priori estimates are given in the framework of weighted spaces.Finally,we prove the existence,uniqueness and regularity of the very weak solutions.

Reweighted Nadaraya-Watson estimation of jump-diffusion models

In this paper,we study the nonparametric estimation of the second infinitesimal moment by using the reweighted Nadaraya-Watson (RNW) approach of the underlying jump diffusion model.We establish strong consistency and asymptotic normality for the estimate of the second infinitesimal moment of continuous time models using the reweighted Nadaraya-Watson estimator to the true function.

An additive-multiplicative rates model for multivariate recurrent events with event categories missing at random

Multivariate recurrent event data arises when study subjects may experience more than one type of recurrent events. In some situations, however, although event times are always observed, event categories may be partially missing. In this paper, an additive-multiplicative rates model is proposed for the analysis of multivariate recurrent event data when event categories are missing at random. A weighted estimating equations approach is developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a model-checking technique is presented to assess the adequacy of the model. Simulation studies are conducted to evaluate the finite sample behavior of the proposed estimators, and an application to a platelet transfusion reaction study is provided.

Weighted estimates for the multilinear singular integral operators with non-smooth kernels

In this paper, by sharp function estimates and certain weak type endpoint estimates, the authors establish some weighted norm inequalities with Ap weights for the multilinear singular integral operators with non-smooth kernels.

Weighted L^p Estimates for Maximal Commutators of Multilinear Singular Integrals

This paper is concerned with the pointwise estimates for the sharp function of two kinds of maximal commutators of multilinear singular integral operators T∑b^＊ and TПb^＊ which are generalized by a weighted BMO function b and a multilinear singular integral operator T, respectively. As applications, some commutator theorems are established.

Shrinkage estimation analysis of correlated binary data with a diverging number of parameters

For analyzing correlated binary data with high-dimensional covariates,we,in this paper,propose a two-stage shrinkage approach.First,we construct a weighted least-squares(WLS) type function using a special weighting scheme on the non-conservative vector field of the generalized estimating equations(GEE) model.Second,we define a penalized WLS in the spirit of the adaptive LASSO for simultaneous variable selection and parameter estimation.The proposed procedure enjoys the oracle properties in high-dimensional framework where the number of parameters grows to infinity with the number of clusters.Moreover,we prove the consistency of the sandwich formula of the covariance matrix even when the working correlation matrix is misspecified.For the selection of tuning parameter,we develop a consistent penalized quadratic form(PQF) function criterion.The performance of the proposed method is assessed through a comparison with the existing methods and through an application to a crossover trial in a pain relief study.

Weighted quantile regression for longitudinal data using empirical likelihood

This paper proposes a new weighted quantile regression model for longitudinal data with weights chosen by empirical likelihood(EL). This approach efficiently incorporates the information from the conditional quantile restrictions to account for within-subject correlations. The resulted estimate is computationally simple and has good performance under modest or high within-subject correlation. The efficiency gain is quantified theoretically and illustrated via simulation and a real data application.

Weighted estimation of single index models with right censored responses

In this paper, the unknown link function, the direction parameter, and the heteroscedastic variance in single index models are estimated by the random weight method under the random censorship, respectively. The central limit theory and the convergence rate of the law of the iterated logarithm for the estimator of the direction parameter are derived, respectively. The optimal convergence rates for the estimators of the link function and the heteroscedastic variance are obtained. Simulation results support the theoretical results of the paper.

Randomly Weighted LAD-Estimation for Partially Linear Errors-in-Variables Models

The authors consider the partially linear model relating a response Y to predictors （x, T） with a mean function x^Tβ0 ＋ g（T） when the x＇s are measured with an additive error. The estimators of parameter β0 are derived by using the nearest neighbor-generalized randomly weighted least absolute deviation （LAD for short） method. The resulting estimator of the unknown vector 30 is shown to be consistent and asymptotically normal. In addition, the results facilitate the construction of confidence regions and the hypothesis testing for the unknown parameters. Extensive simulations are reported, showing that the proposed method works well in practical settings. The proposed methods are also applied to a data set from the study of an AIDS clinical trial group.