尖峰态分布

词组短语

leptokurtic distribution

与尖峰态分布相关的网络例句 [注：此内容来源于网络，仅供参考]

This paper is expanded into three main parts to study and analyze the stock time series.1 Through the normality tests, All the sample stock-data curves show different phenomenon of Aiguille and Fat-tail, as well as Left-Deviation, which proves that none of the sample series belong to normal distribution, in other words, the linear method can't be applied into the analysis process, so we adopted the R/S method instead.2 The application of R/S method has verified the long-term memory characteristics of the sample series, and Support Vector Regression is combined to enhance the precision of Average circle period.

文中对股票时间序列从以下三个方面进行了研究和分析：1通过正态性检验，发现各样本股票数据均表现出不同程度的尖峰和胖尾以及左偏现象，证明了所有的样本序列全都不属于正态分布，也就是说明它们不能够适用线性方法进行分析，必须采用重标极差分析方法的非线性方法。2应用R/S分析方法验证他们具有长期记忆的特性，并且结合支持向量回归机提高了测定平均循环周期时的精确性。

The following conclusions have been made in this paper.(1) Based on CO_2 flux data of eddy covariance, variation characteristics of gross primary productivity in four flux observation stations were studied, which are an alpine meadow, an alpine shrub meadow, a swamp alpine meadow and a steppe alpine meadow at Dongxiong. The results show that photosynthetic capacity of the alpine meadow is the highest, and the annual total GPP is 652.2g C/m~2. Daily-differencing approach is used to analyze the random error of CO_2 fluxes measurements. The results show that the distribution of random error follows more closely follows a double-exponential, rather than a normal distribution, capturing the high peak and thick tail, and the random error varies with environment variables, which violates the assumptions for the ordinary least squares fitting with normality and homoscedasticity, consequently, we introduce maximum likelihood method for parameter optimization.

本文主要在以下几个方面开展工作并获得了一些认知和结论：(1)通过分析样带区域内高寒草甸、高寒灌丛、沼泽化湿地和草原化高寒草甸四个通量观测站点草地生态系统总初级生产力变化特征，研究结果表明HBBT矮嵩草草甸生态系统植被光合作用能力较强，年GPP总量为652.2 gC/m~2，明显高于其他三种生态系统；通过利用"单塔日变化法"获得四站点通量观测数据随机误差，结果表明通量观测随机误差概率分布呈现尖峰厚尾的特征，与正态分布相比，更服从双边指数分布，进一步分析表明通量观测随机误差随环境变量(风速、温度和光合有效辐射)的变化而变化，这违背了普通最小二乘法进行生态过程模型参数优化正态分布且误差同质的假设，因此本研究中引入最大似然法进行生态过程模型参数优化。

Using a variety of statistical methods tested what rate of assets return are not subject to the normal distribution assumption, but leptokurtic distribution, and week data is closer to the normal distribution than daily data.

采用多种统计方法检验了资产收益率并不服从正态分布假设，而是尖峰厚尾分布，且周收益率要比日收益率更接近正态分布。

In the empirical analysis part, statistical descriptions on the sample funds are made firstly, which show that the actual distribution of each sample fund"s daily net value return possesses the characteristic of leptokurtosis. So it is necessary to add student T distribution and GED to capture such leptokurtosis characteristic other than normal distribution. Secondly, ARCH test shows that there exists volatility clustering in each sample fund"s daily net value return, so GARCH related models should be used to describe such volatility clustering characteristic.

实证分析部分首先对样本基金进行统计描述，得出其收益序列均存在尖峰厚尾特征，不服从正态分布，因此有必要在下面的VaR计算中加入T分布和GED分布来捕捉这种尖峰厚尾特征；并且经ARCH检验后得出收益序列存在明显的波动聚集性的特征，因此可以选择GARCH类模型来描述这种特性，经过模型筛选，得出最适合我国开放式股票型基金的收益波动性模型为GARCH(1，1)模型。

Chinese stock market presents leptokurtic and fat tails, it does not follow normal distribution but fractal distribution.

中国股市呈现尖峰胖尾特性，不服从正态分布，而服从更一般。。。

The results show three outstanding characters: these two stock markets are closely correlated; the overall day return doesn\'t follow the normal distribution, but it is characterized with leptokurtic and fat tail; the security market return has stable characteristics and multi-fractal characteristics which shown as the long memory, fat tail, scale properties, and variability.

发现上海和深圳两个证券市场的关联程度非常大；中国证券市场中的总体日收益率分布不服从正态分布，而是呈&尖峰态&，并且具有&厚尾&的特征；证券市场收益序列呈现稳态特征与多标度分形特性，表现为长期记忆特性、厚尾特性、标度特性和易变性。

In this paper, we contribute an evolutionary heterogeneous beliefs model byusing t distribution to replace traditional standard normal to describe fundamen-tal price process and adding risk-adjusted market fraction function in classicaltwo types traders scheme. And then we utilizedifference equation stability and bifurcation theory and numerical simulation tostudy the system. It is found that the system has some styled facts (high kurto-sis、fat tali and long memory) of the actual financial market, and this indicatesthat the simulation model can reflect well the true financial market.

本文通过引入t分布代替原有的正态分布描述基础价格过程，引入经风险调整的投资者市场分数维函数取代原有的无风险调整的市场分数维函数，在经典的两类投资者(自主投资者和图表分析者)模拟模型框架下，建立了新的异质预期资产定价模型，利用差分方程稳定性和分支理论及数值模拟的方法对该系统进行理论分析和实证研究，发现模型具有真实金融市场的程式化事实(尖峰厚尾性，长记忆性等)，模拟效果较好。

Preliminary data analysis shows that the return rates distribution of SSE is fat-tailed and doesn't obey normal distribution and there is"leverage effect"in Shanghai Stock market.

基本统计分析发现，上证综合指数回报率分布存在尖峰肥尾性，不服从正态分布，并且还具有杠杆效应。

In view of the peaked and fat-tailed characteristics of financial return data distribution and its effect of clustering fluctuation and especially the "leverage effect" of fluctuation on VaR estimates and some efficiencies when estimating VaR with various assumptions of return data distribution,a semi-parameter approach based on EGARCH-VaR model is developed.

在综合考虑了金融收益数据分布的尖峰厚尾特征及其波动集群性，尤其是其波动的&杠杆效应&对VaR估计的影响以及各种假定收益率分布在计算风险价值时存在不足的基础上，提出了基于EGARCH-VaR的半参数方法，并且与正态分布和t分布假设下的GARCH模型的VaR计量方法进行比较，通过实证分析，并利用后验测试，表明基于EGARCH-VaR的半参数方法对风险价值的测度优于正态分布和t分布假设下GARCH模型的VaR计量方法。

In the article we at first discuss the kind of stock log-return distribution. We find that 7 kinds function can describe stock log-return form their works: Gausses of normal distribution, Levy distribution, t distribution, spike attitude distribution, random fluctuating model, ARCH-GARCH model, divide shape Brownian movement .

本文首先讨论了股票收益分布的种类，作者对前人的工作综合发现目前描述股票收益的函数大概有七种：高斯正态分布、利维稳定分布、t标度分布、尖峰态分布、随机波动率模型、ARCH—GARCH模型、分形布朗运动，我们分别对这七种函数进行了简要的介绍。

与尖峰态分布相关的网络解释 [注：此内容来源于网络，仅供参考]

Kurtosis：峰度

格式:SKEW18 峰度(kurtosis)是指数据分布集中趋势高峰的形状,它通常是与标准正态分布相比较而言的. 若分布的形状比标准正态分布更瘦更高,成为尖峰分布. 相反,若更扁平,称为平峰分布.

length of tangent：切线的长;切距

法线的长;法距 length of normal | 切线的长;切距 length of tangent | 尖峰态分布 leptokurtic distribution

lens space：透镜空间

lens 透镜 | lens space 透镜空间 | leptokurtic distribution 尖峰态分布

leptokurtic distribution：尖峰态分布

leptogeosyncline 薄地槽 | leptokurtic distribution 尖峰态分布 | leptokurtosis 峰态

leptokurtic distribution：尖峰态分布,凸峰态分布

leptocentric vascular bundle 韧皮中心维管束 | leptokurtic distribution 尖峰态分布,凸峰态分布 | lepton 轻子

less than or equal：小于或等于

leptokurtic distribution 尖峰态分布 | less than or equal 小于或等于 | letter 文字

leptocentric vascular bundle：韧皮中心维管束

leprous facial paralysis 麻风性面瘫 | leptocentric vascular bundle 韧皮中心维管束 | leptokurtic distribution 尖峰态分布,凸峰态分布

leptokurtic distribution：尖峰态分布,凸峰态分布

leptocentric vascular bundle 韧皮中心维管束 | leptokurtic distribution 尖峰态分布,凸峰态分布 | lepton 轻子