随机波动模型的参数估计方法

随机波动(SV)模型是研究金融收益率波动性的一种重要模型,但该模型没有精确的似然函数表达式,对其进行研究具有一定的挑战性.将近十几年来国内外学者提出的不同参数估计方法分为2类,讨论了各种方法的优缺点,并对其给予了评价.     

Large-sample confidence intervals for validity and reliability coefficients.

Large-sample confidence intervals (CI) for reliability, validity, and unattenuated validity are presented. The CI for unattenuated validity is based on the Bonferroni inequality, which relies on one CI for test–retest reliability and one for validity. Covered are four reliability–validity situations: (a) both estimates were from random samples; (b) reliability was from a random sample but validity was from a selected sample; (c) validity was from a random sample but reliability was from a selected sample; and (d) both estimates were from selected samples. All CIs were evaluated by using a simulation. CIs on reliability, validity, or unattenuated validity are accurate as long as selection ratio is at least 20% and selected sample size is 100 or larger. When selection ratio is less than 20%, estimators tend to underestimate their parameters. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

一种新型实用热轧工艺参数模型的开发

介绍了一套自主开发的热轧工艺参数模型．该模型内耦合了不同钢种的变形抗力曲线,这些变形抗力方程中耦合了钢的化学成分、温度、应变、应变速率及奥氏体晶粒尺寸等因素．根据输入的工艺参数用西姆斯方程计算每道次的应变速率及应变量,并得到相应道次的变形抗力、热轧轧制力、力矩及功率等参数．模型可根据实测的结果自学习,并修正相应的结果．与攀钢热轧厂的实测结果相比,模型的输出结果吻合较好,预测误差在10%以内．     

A new stochastic model for auditory-nerve discharge

This paper investigates the mechanisms for history-dependent probability of eight-nerve discharge, which is modeled as the probability that the excitatory postsynaptic potential (EPSP) process crosses afferent membrane threshold, with the discharge history dependence due to the dependence of postsynaptic threshold voltage on time since previous action potential. The model parameters are the Poisson intensity alpha t of vesicle release, the duration epsilon and probability density PV(upsilon) of single-vesicle EPSP's, and the threshold voltage curve theta (tau) for spiking. It is proven that the infinitesimal conditional probabilities of discharge exhibit two distinct behaviors. The first is associated with the time tau = T D, exactly the time the neuron releases from absolute refractory where there is no intensity [theta(tau) = infinity, for tau < T D]. At this time the neuron has a nonzero probability of discharge [symbol:see text] (T D) = lim delta-->0 Pr(Nt,t+delta = 1/t - wNt = T D). The second regime corresponds to the time since previous spike being greater than dead time, tau > T D, during which time the intensity exists lambda t(tau) = lim delta-->0(1/delta) Pr(Nt,t+delta = 1/t - wNt = tau > T D). The fact that there is a nonzero probability of discharge following passage from the absolute refractory period predicts the nonmonotonic hazard intensity seen in high spontaneous neurons [R. P. Gaumond, Ph.D. thesis, Washington University, St. Louis (1980)] and high driven rate neurons. It is shown that for the lowest range of vesicle release intensities where the vesicle-release-rate/membrane-integration-time product alpha t epsilon small, the nonzero probability of discharge at a point is approximately equal to 0. The discharge intensity is dominated by a term linear in vesicle release intensity: lambda t(tau) approximately alpha t exp(-integral of t-epsilon t alpha sigma d sigma) integral of theta (tau) infinity Pv(upsilon)d upsilon. This is precisely the Siebert-Gaumond intensity product form with monotonic recovered discharge probability. At high vesicle release rates, such as for driven rate responses, the nonzero probability of discharging at a point becomes of nonsignificant size, and the intensity of discharge grows nonlinearly with alpha t, implying the product model does not hold. The model is demonstrated via the analysis of auditory nerve fibers from the cat.     

Estimating the rate constants in a two-compartment stochastic model

A procedure is described for estimating the rate constants of a two-compartment stochastic model for which the covariance structure over time of the observations is known. The proposed estimation procedure, by incorporating the known (as a function of the parameters to be estimated) covariance structure of the observations, produces regular best asymptotically normal (RBAN) estimators for the parameters. In addition, the construction of approximate confidence intervals and regions for the parameters is made possible by identification of the asymptotic covariance matrix of the estimators. The explicit form of the inverse of the covariance matrix, which is required in the estimation procedure, is presented. The procedure is illustrated by application to real as well as simulated data, and a comparison is made to the widely used nonlinear least squares procedure, which does not account for correlations over time.     

Testing a model for the dynamics of actin structures with biological parameter values

A simple mathematical model for the dynamics of network-bundle transitions in actin filaments has been previously proposed and some of its mathematical properties have been described. Other models in this class have since been considered and investigated mathematically. In this paper, we have made the first steps in connecting parameters in the model with biologically measurable quantities such as published values of rate constants for filament-crosslinker association. We describe how this connection was made, and give some preliminary numerical simulation results for the behavior of the model under biologically realistic parameter regimes. A key result is that filament length influences the bundle-network transition.     

A stochastic model for early HIV-1 population dynamics

Histozoic and coelozoic skrjabillanid-type nematodes belonging to the genera Skrjabillanus, Molnaria, Sinoichthyonema, Esocinema, Daniconema and Lucionema are recorded from the subcutaneous tissues, fins, swimbladder and abdominal cavity of different fish species living in natural waters and fish farms of Hungary. In addition to the nine taxonomically identified parasite species, one Skrjabillanus sp., two Molnaria spp. and three Esocinema spp. were identified to the genus level only. The histozoic larval stages of a Molnaria, Daniconema and Lucionema species each, living in a site different from that of the imagoes, were also detected. The presence of closely not identified first- to third-stage skrjabillanid larvae was demonstrated in 26.3% of the parasitic carp lice (Argulus foliaceus L.) collected from the eight fish species.     

数据驱动的卷取温度模型参数即时自适应设定算法

为提高热轧换规格首块钢头部卷取温度命中率,采用数据挖掘技术,从历史带钢冷却数据中推断出与实际带钢相匹配的卷取温度模型水冷换热学习系数,并将其应用于模型预设定计算。首先,对冷却特征参数进行识别,按照相对型、绝对型、相等型和策略型四种方式进行定义,并对实际带钢与历史带钢的各项冷却特征参数进行相似距离计算。当历史带钢的总相似距离满足要求时,将其聚类为实际带钢的相似卷,并考虑各相似卷的时间影响,计算相似权重值;随后,基于相似带钢的头部和尾部信息,建立由卷取温度预报误差、偏离学习系数回归值惩罚项和偏离默认值惩罚项等构成的目标函数以及相应的约束条件,采用梯度下降法求解该二次规划问题,通过三次优化逐步计算出学习系数参考值和表征学习系数与带钢速度及目标卷取温度呈双线性关系的两个参数;最后,根据实际带钢的穿带速度、目标卷取温度等冷却条件计算冷却设定所需的学习系数。现场应用表明:基于十万块历史带钢冷却数据驱动的模型参数即时自适应设定算法可增强卷取温度模型对带钢头部冷却的预设定能力,学习系数即时自适应设定能力随着内存中保存的历史带钢冷却数据的多样性和检索出的相似卷数量的增加而提升。      

A stochastic model of preference change and its application to 1992 presidential election panel data.

The authors present and test a model for the evolution of preferences. Personal preferences are represented by rankings with possible ties and are posited to change under the influence of "tokens" of information in the environment. These tokens may not be directly controlled or observed by the researcher. The authors apply the model to 1992 National Election Study panel data (W. E. Miller, D. R. Kinder, S. J. Rosenstone, & NES, 1993). The parameter estimates suggest that negative campaigning played a major role in the information flow. Democrats and Republicans experienced a barrage of contradicting information about Perot; Democrats, Republicans, and Independents each received or perceived different information. A shift in the perception of the candidates led the Republicans to evaluate Bush and Perot less favorably after the election. These results demonstrate the model's potential to analyze persuasion as a real-time stochastic process and without a media content analysis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)