一类二阶拟线性微分方程的非振动定理

考虑二阶拟线性微分方程 :( |y′|α -1y′)′+q(t) |y|α -1y=0 ,获得了在 q(t)振动的条件下该方程非振动的一些充分 (必要 )条件 ,改进和推广了已有文献的结果     

Moments of the sampled autocovariances and autocorrelations for a Gaussian white-noise process

Cumulants, moments about zero, and central moments are obtained for the mean-corrected serial covariances and serial correlations for series realizations of length n from a white-noise Gaussian process. All first and second moments (and some third, fourth, and higher moments) are given explicitly for the serial covariances; and the corresponding moments for the serial correlations are derived either explicitly or implicitly.     

Approach to normality of mean and M-estimators of location

The approach to normality of an estimate is displayed graphically by the nonlinearity in the derivative of the log density, and this nonlinearity is related to the accuracy of the normal approximation for the tail area. Using techniques from small-sample asymptotics, an alternate proof of the central limit theorem is given and same indices of tail behavior are examined.     

广义导数及其应用

文章提出了一种广义导数的概念,得到了广义导数的运算法则,以及连续函数的中值定理。     

Block empirical likelihood for longitudinal partially linear regression models

The authors propose a block empirical likelihood procedure to accommodate the within‐group correlation in longitudinal partially linear regression models. This leads them to prove a nonparametric version of the Wilks theorem. In comparison with normal approximations, their method does not require a consistent estimator for the asymptotic covariance matrix, which makes it easier to conduct inference on the parametric component of the model. An application to a longitudinal study on fluctuations of progesterone level in a menstrual cycle is used to illustrate the procedure developed here.     

阶乘幂多项式及其基本恒等式

考虑两类阶乘幂多项式，由向前或向后差分公式，得到两个同类阶乘幂多项式等价的充分必要条件．给出并证明了阶乘幂代数系统的两类基本恒等式，一类是阶乘幂的二项式定理；另一类是同阶阶乘幂之差的因式分解定理(乘方差定理)．     

Modeling cooperation among self-interested agents: a critique

This paper reviews recent attempts to model cooperation in groups of self-interested unrelated individuals. It concludes that no empirically plausible model has been developed, and the economist's quest for an explanation of human prosociality in terms of self-interested agents has not been successful. We show that the equilibrium criteria employed in standard game-theoretic models have no attractive dynamic properties, and the mechanisms deployed in these models are not generally seen in empirical examples of cooperative groups. We also show that these models are not robust to plausible rates of signaling error and mutation. We present an alternative, involving strongly reciprocal agents, that satisfies all the requirements for a dynamically stable model of cooperation.     

On asymptotic distribution of prediction in functional linear regression

There has been substantial recent attention on problems involving a functional linear regression model with scalar response. Among them, there have been few works dealing with asymptotic distribution of prediction in functional linear regression models. In recent literature, the centeral limit theorem for prediction has been discussed, but the proof and conditions under which the random bias terms for a fixed predictor converge to zero have been ignored so that the impact of these terms on the convergence of the prediction has not been well understood. Clarifying the proof and conditions under which the bias terms converge to zero, we show that the asymptotic distribution of the prediction is normal. Furthermore, we have derived those results related to other terms that already obtained by others, under milder conditions. Finally, we conduct a simulation study to investigate performance of the asymptotic distribution under various parameter settings.     

Examples of the use of an equivalence theorem in constructing optimum experimental designs for random-effects nonlinear regression models

The problem considered is that of finding an optimum measurement schedule to estimate population parameters in a nonlinear model when the patient effects are random. The paper presents examples of the use of sensitivity functions, derived from the General Equivalence Theorem for D-optimality, in the construction of optimum population designs for such schedules. With independent observations, the theorem applies to the potential inclusion of a single observation. However, in population designs the observations are correlated and the theorem applies to the inclusion of an additional measurement schedule. In one example, three groups of patients of differing size are subject to distinct schedules. Numerical, as opposed to analytical, calculation of the sensitivity function is advocated. The required covariances of the observations are found by simulation.     

On rates of convergence to infinitely divisible laws for dependent random variables

Large O and small o approximations of the expected value of a class of functions (modified K-functional and Lipschitz class) of the normalized partial sums of dependent random variables by the expectation of the corresponding functions of infinitely divisible random variables have been established. As a special case, we have obtained rates of convergence to the Stable Limit Laws and to the Weak Laws of Large Numbers. The technique used is the conditional version of the operator method of Trotter and the Taylor expansion.