A UNIFIED FRAMEWORK FOR MODELLING WILDLIFE POPULATION DYNAMICS†

This paper proposes a unified framework for defining and fitting stochastic, discrete‐time, discrete‐stage population dynamics models. The biological system is described by a state‐space model, where the true but unknown state of the population is modelled by a state process, and this is linked to survey data by an observation process. All sources of uncertainty in the inputs, including uncertainty about model specification, are readily incorporated. The paper shows how the state process can be represented as a generalization of the standard Leslie or Lefkovitch matrix. By dividing the state process into subprocesses, complex models can be constructed from manageable building blocks. The paper illustrates the approach with a model of the British grey seal metapopulation, using sequential importance sampling with kernel smoothing to fit the model.     

基带升余弦谱波形的数字实现及其分析

介绍了二进制横向滤波器(BTF)直接实现数据限带传输升余弦谱脉冲波形成形的方法。着重研究了影响横向滤波器结构的参数(每波特内取样数 K,单位冲激响应波形旁瓣数 L)与BTF 的阻带衰减和所形成升余弦谱信号在最坏情况下的码间干扰之间的关系,为优化工程设计提供了依据。最后给出了数值实例和实验结果。     

Spectral properties of the concurrent and forecasting seasonal linear filters of the X-11-ARIMA method

This study analyzes the properties of the linear filters of the X-11-ARIMA seasonal adjustment method applied for current seasonal adjustment. It provides the general formula for the combined weights that result from the ARIMA model extrapolation filters with the X-11 seasonal-adjustment filters. The three cases studied correspond to the three ARIMA models automatically tested by the X-11-ARIMA program, namely, (0, 1, 1)(0, 1, 1), (0, 2, 2)(0, 1, 1), and (2, 1. 2)(0, 1,1). The parameter values chosen reflect different degrees of flexibility of the trend-cycle and seasonal components. It is shown that the X-11-ARIMA linear filters for current seasonal adjustment are very flexible; they change with both the ARIMA extrapolation model and its parameter values, contrary to those of the X-11 program, which are fixed for a given set of options.     

辛德瑞拉的水晶鞋与子爵的缎烟盒--也说"灰姑娘情结"与爱玛的悲剧

<灰姑娘>中的辛德瑞拉丢下水晶鞋逃遁而去最终等来了王子和爱情幸福,<包法利夫人>中的爱玛捡到了子爵的缎烟盒后渴望着与某一个子爵一类的男人共同改写婚姻的庸俗无聊,最终走进爱情幻灭自杀的悲剧结局.在男权主宰的社会历史文化传统中,"水晶鞋"和"缎烟盒"这两个语言符号的实际所指是相同的,即女性共同的集体无意识--"灰姑娘情结",以及这一情结所导向的女性在历史的、现实的存在过程中丧失主体性的悲剧陷阱.     

彩色图像脉冲噪声的自适应矢量滤波

提出了基于噪声像素检测的自适应矢量滤波器的新方法,该法对图像中噪声像素进行检测时,仅对噪声像素进行矢量滤波,而对非噪声像素则保持其原值不变,并可根据图像噪声情况自适应地选择滤波窗口。新滤波器能有效地滤除彩色随机脉冲噪声,并保持图像边缘与细节,其性能优于经典的矢量中值滤波器、方向一距离滤波器、方向一幅度矢量滤波器等非线性滤波器。     

SAR图像的边沿检测方法研究

针对合成孔径雷达（SAR）图像的特点,利用小波变换的高频分量能反映边沿细节的特性,提出了一种结合小波变换、中值滤波、自适应门限判决和微分梯度算子的边沿检测方法。实验结果表明,该方法具有良好的边沿检测效果和稳定性。     

一类混沌经济模型的控制方法的研究

就经济系统中的一类多参数混沌模型 ,应用微分动力学理论 ,研究了变参数结构反馈控制方法 ,成功地进行了多参数混沌控制 .并讨论小波滤波法 ,成功实现了混沌不稳定周期轨道的稳定控制方法 ,并以具体的财产管理混沌模型为例验证所提出的方法     

H公式与完全相关数对

定义完全相关数对并推导出求完全相关数对个数的H公式。     

A new robust Kalman filter for filtering the microstructure noise

We propose a robust Kalman filter (RKF) to estimate the true but hidden return when microstructure noise is present. Following Zhou's definition, we assume the observed return Y_t is the result of adding microstructure noise to the true but hidden return X_t. Microstructure noise is assumed to be independent and identically distributed (i.i.d.); it is also independent of X_t. When X_t is sampled from a geometric Brownian motion process to yield Y_t, the Kalman filter can produce optimal estimates of X_t from Y_t. However, the covariance matrix of microstructure noise and that of X_t must be known for this claim to hold. In practice, neither covariance matrix is known so they must be estimated. Our RKF, in contrast, does not need the covariance matrices as input. Simulation results show that the RKF gives essentially identical estimates to the Kalman filter, which has access to the covariance matrices. As applications, estimated X_t can be used to estimate the volatility of X_t.     

A modification of the Whittaker–Henderson method of graduation

This paper presents a modified Whittaker–Henderson (WH) Method of Graduation. After giving a closed-form solution, we show that it is of practical use because it provides not only a smoothed series identical to that of the WH graduation, but also an extrapolation beyond the sample limit of current data. In addition, we introduce two other penalized least squares problems and show that they provide the same results as those of the modified WH graduation.