基于CGR的DNA序列的时间序列模型(英文)

利用DNA序列的混沌游戏表示(chaos game representation,CGR),提出了将2维DNA图谱转化成相应的类谱格式的方法。该方法不仅提供了一个较好的视觉表示,而且可将DNA序列转化成一个时间序列。利用CGR坐标将DNA序列转化成CGR弧度序列,并引入长记忆ARFIMA(p,d,q)模型去拟合此类序列,发现此类序列中有显著的长相关性且拟合度很好。     

The dual klepsydra model of internal time representation and time reproduction

We present a model of the internal representation and reproduction of temporal durations, the 'dual klepsydra' model (DKM). Unlike most contemporary models operating on a 'pacemaker-counter' scheme, the DKM does not assume an oscillatory process as the internal time-base. It is based on irreversible, dissipative processes in inflow/outflow systems (leaky klepsydrae), whose states are continuously compared; if their states are equal, durations are subjectively perceived as equal. Model-based predictions fit experimental time reproduction data with good accuracy, and show qualitative features not accounted for by other models. The deterministic model is characterized by two parameters, kappa (outflow rate coefficient) and eta (ratio of inflow rates). A stochastic version of the model (SDKM) assumes randomly fluctuating inflows, involves two more parameters, and accounts for intra-individual variance of reproduced durations. Analysis of the SDKM leads to non-trivial problems in the stochastic theory, briefly sketched here. Methods of parameter estimation for both deterministic and stochastic versions are given. Applying the DKM to the subjective experience of time passage, we show how subjective measure of elapsed time is constituted. Finally, essential features of the model and its possible neurophysiological interpretation are discussed.     

Structural time series models with feedback mechanisms

Structural time series models have applications in many different fields such as biology, economics, and meteorology. A structural times series model can be represented as a state-space model where the states of the system represent the unobserved components and the structural parameters have clear interpretations. This paper introduces a class of structural time series models that incorporate feedback from the latent components of the history. An iterative procedure is proposed for estimation. These models allow flexible and robust feedback mechanisms, have clear interpretations, and have a computationally efficient estimation procedure. They are applied to hormone data to characterize hormone secretion and to explore a potential feedback mechanism.