The optimum growth rate for population reconsidered

This article gives exact general conditions for the existence of an interior optimum growth rate for population in the neoclassical two-generations-overlapping model. In an economy where high (low) growth rates of population lead to a growth path that is efficient (inefficient), there always exists an interior optimum growth rate for population. In all other cases, there exists no interior optimum. The Serendipity Theorem, however, does, in general, not hold in an economy with government debt. Moreover, the growth rate for population that leads an economy with debt to a golden rule allocation can never be optimal.      

一类具二维核共振条件非局部边值问题

利用Mawhin的重合度理论得到一类二阶非线性微分方程多点边值问题在二维核共振条件下解的存在性。     

Riemann-Lebesgue引理

对Ｌｅｂｅｓｇｕｅ可积函数空间中的Ｒｉｅｍａｎｎ－Ｌｅｂｅｓｇｕｅ引理及其推广形式进行了讨论，证明和推广了一些结果     

Randomized response model in a matched pair study

The development of randomized response models for personal interview surveys has attracted much attention since the pioneering work of Warner [1965. Randomized response: a survey technique for eliminating evasive answer bias. J. Amer. Statist. Assoc. 60, 63–69]. Several randomized response models have been developed by researchers for collecting data on both qualitative and the quantitative variables, but none of these models discuss matched pair data. In this paper, we develop a new randomized response model and study its application to an important political question.     

一个Smarandache问题的均值估计

阐述了一个Smmandache问题的均值估计，并利用Perron公式和留数定理，给出了这个问题的一个渐近公式，其余项可以做到D（x^V2）．     

关于Arzela定理的条件的减弱

证明了在Arzela定理中 ,将函数列 (fn) ∞n =1在一闭的有界区间 [a ,b]上一致有界减弱为“弱一致有界”时 ,定理的结论仍成立     

Nonlinear functional models for functional responses in reproducing kernel hilbert spaces

The author proposes an extension of reproducing kernel Hilbert space theory which provides a new framework for analyzing functional responses with regression models. The approach only presumes a general nonlinear regression structure, as opposed to existing linear regression models. The author proposes generalized cross‐validation for automatic smoothing parameter estimation. He illustrates the use of the new estimator both on real and simulated data.     

On the statistical inference of a machine-generated autoregressive AR(1) model

We have obtained the asymptotic bias and the limiting distribution for the Yule–Walker estimator of the autoregressive parameter under a considerably weaker assumption than that of independence in the noise sequence. Among other things, these suggest robustness of the classical results and throw some light on the use of simulations based on pseudorandom numbers in verifying these results.