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对于纵向数据下半参数回归模型,基于广义估计方程和一般权函数方法构造了模型中参数分量和非参数分量的估计.在适当的条件下证明了参数估计量具有渐近正态性,并得到了非参数回归函数估计量的最优收敛速度.通过模拟研究说明了所提出的估计量在有限样本下的精确性. 相似文献
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纵向数据变系数模型常应用于传染病学、生物医学和环境科学等领域. 本文提出了一种称为减元估计法的方法来估计模型中的未知函数和它们的导数. 减元估计法既适用于系数函数具有相同光滑度的情形, 也适用于系数函数具有不同光滑度的情形; 既适用于变量不依赖于时间的情形, 也适用于变量依赖于时间的情形. 给出了一般条件下估计量的局部渐近偏差、方差和渐近正态性, 并且渐近性结果显示: 当系数函数具有不同的光滑度时, 减元估计量的渐近方差比现有方法得到的估计量的渐近方差要少. 本文还通过 Monte Carlo 模拟研究了估计量的有限样本性质. 相似文献
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本文研究了空间数据变系数部分线性回归中的分位数估计. 模型中的参数估计量通过未知系数函数的分段多项式逼近得到, 而未知系数函数的估计量通过将参数估计量代入模型中并通过局部线性逼近得到. 文中推导了未知参数向量估计量的渐近分布, 并建立了未知系数函数估计量在内点及边界点的渐近分布. 通过Monte Carlo 模拟研究了估计量的有限样本性质. 相似文献
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为了克服在单参数双边截断型分布族中不能消除参数估计中在Bahardur意义下的超有效病态现象,本文提出了一种新的渐近效率.根据这种效率的定义,对一般 的参数函数,构造了适用的渐近中位无偏的渐近有效估计量.作为全文的理论基础,我们发现了渐近中位无偏估计的最优收敛速度. 相似文献
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研究一类新的半参数回归模型回归函数的核估计问题,其中误差项为一阶非参数自回归过程.通过重复利用Watson-Nadaraya核估计方法,构造了回归函数及误差回归函数的估计量分别为β,g(·)和ρ(·),在适当的条件下,证明了估计量β,g(·)和ρ(·)的渐近正态性. 相似文献
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研究一类新的非参数回归模型回归函数的核估计问题,其中误差项为一阶非参数自回归方程.通过重复利用Watson-Nadaraya核估计方法,构造了回归函数及误差回归函数的估计量分别为m(.)和ρ(.),在适当的条件下,证明了估计量m(.)和ρ(.)的渐近正态性. 相似文献
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张巍巍 《数学的实践与认识》2021,(3):128-135
研究半参数部分线性变系数模型的有偏估计,当回归模型参数部分自变量存在多重共线性时,在随机线性约束条件下,融合Profile最小二乘估计、加权混合估计和Liu估计构造回归模型参数分量改进的加权混合Profile-Liu估计,并在一定正则条件下证明估计量的渐近性质,最后利用蒙特卡洛数值模拟验证所提出估计量的有限样本表现性. 相似文献
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唐庆国 《高校应用数学学报(A辑)》2009,24(1)
提出了一种叫做逐元估计法的方法用来估计变系数模型中的未知函数和它们的导数,构造了一种快速选择估计量窗宽和快速计算大量估计点的方法,推导了估计量的渐近正态性.通过Monte Carlo模拟研究了估计量的有限样本性质. 相似文献
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本文研究了金融风险管理理论中风险价值(VaR)的非参数核光滑估计和经验估计的效率问题.对非独立的时间序列损失/收益样本,在均方误差(MSE)准则的意义下引入亏量的概念,亏量越大表明估计效率越低.并利用亏量对VaR模型的核光滑估计和基于样本分位数的经验估计进行了比较,在理论上证明了VaR模型的核光滑估计优于经验估计.同时,通过计算机模拟证实了理论获得的结论.本文还对国内沪深两市上的证券投资基金进行了实证分析,计算了样本基金的VaR风险度量的经验估计和核光滑估计,并计算了样本基金基于周收益率和VaR估计的风险调整收益(RAROC)值,以此对样本基金的业绩做出了有用的评价. 相似文献
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We consider the periodic generalized autoregressive conditional heteroskedasticity(P-GARCH) process and propose a robust estimator by composite quantile regression. We study some useful properties about the P-GARCH model. Under some mild conditions, we establish the asymptotic results of proposed estimator.The Monte Carlo simulation is presented to assess the performance of proposed estimator. Numerical study results show that our proposed estimation outperforms other existing methods for heavy tailed distributions.The proposed methodology is also illustrated by Va R on stock price data. 相似文献
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金融数据呈现的厚尾性已达成共识。本文首先基于指数回归模型提出了一种厚尾分布的极值分位数估计方法,得到了在险风险值的估计公式。然后得到了上海上证指数、国债指数和企业债券指数的在险风险值的估计值,比较了他们的极值风险. 相似文献
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一类重尾风险因子的模拟及其投资高风险值和置信区间的估计 总被引:2,自引:0,他引:2
由于金融市场中的日周期或短周期对数回报率的样本数据多数呈现胖尾分布,于是现有的正态或对数正态分布模型都在不同程度上失效,为了准确模拟这种胖尾分布和提高投资风险估计及金融管理,本文引进了一种可根据实际金融市场数据作出调正的蒙特卡洛模拟方法.这个方法可以有效地复制金融产品价格的日周期对数回报率数据的胖尾分布.结合非参数估计方法,利用该模拟方法还得到投资高风险值以及高风险置信区间的准确估计。 相似文献
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1. Introductionffendomly truncated data frequently arise in medical studies; other application areas include economics, insurance and astronomy in a broad senses random truncation correspondsto biased sampling, where only partial or incomplete data are aVailable about the variableof interest. A typical realization. can occur as follows: Suppose that individuals/items experience tWO consecutive events in time, an initiating eveal at t and a terminating eveal ats. Usuajly, statistical niterest … 相似文献
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Pavel Čížek 《Applications of Mathematics》2008,53(3):267-279
The paper studies a new class of robust regression estimators based on the two-step least weighted squares (2S-LWS) estimator
which employs data-adaptive weights determined from the empirical distribution or quantile functions of regression residuals
obtained from an initial robust fit. Just like many existing two-step robust methods, the proposed 2S-LWS estimator preserves
robust properties of the initial robust estimate. However, contrary to the existing methods, the first-order asymptotic behavior
of 2S-LWS is fully independent of the initial estimate under mild conditions. We propose data-adaptive weighting schemes that
perform well both in the cross-section and time-series data and prove the asymptotic normality and efficiency of the resulting
procedure. A simulation study documents these theoretical properties in finite samples. 相似文献
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J. David Cabedo Semper Ismael Moya Clemente 《European Journal of Operational Research》2003,150(3):21
In this paper we put forward a new method to estimate value at risk (VaR), autoregressive conditional heteroskedastic (ARCH) factor, which combines multivariate analysis with ARCH models. Firstly, from a set of correlated portfolio risk factors, we derive a smaller uncorrelated risk factors set, by applying multivariate analysis. Secondly, we use ARCH schemes to model uncorrelated factors historical behaviour. Thirdly, we use the estimated models to predict future values for factors standard deviation. From them, VaR calculation is immediate. In this way, ARCH factor methodology overcomes the multivariate ARCH models drawbacks, which, in practice, make these unworkable for VaR calculation purposes. We apply the proposed methodology over a set of foreign exchange risk exposed portfolios, obtaining better results than those reached when J.P. Morgan’s Riskmetrics is used. 相似文献
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Rania Jammazi Duc Khuong Nguyen 《The Journal of the Operational Research Society》2017,68(11):1352-1362
This article proposes a wavelet-based extreme value theory (W-EVT) approach to estimate and forecast portfolio’s Value-at-Risk (VaR) given the stylized facts and complex structure of financial data. Our empirical application to portfolios of crude oil prices and US dollar exchange rates shows that the W-EVT models provide an effective and powerful tool for gauging extreme moments and improving the accuracy of portfolio’s VaR estimates and forecasts after noise is removed from the original data. 相似文献
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Among recent measures for risk management, value at risk (VaR) has been criticized because it is not coherent and expected shortfall (ES) has been criticized because it is not robust to outliers. Recently,[Math. Oper. Res., 38, 393-417 (2013)] proposed a risk measure called median shortfall (MS) which is distributional robust and easy to implement. In this paper, we propose a more generalized risk measure called quantile shortfall (QS) which includes MS as a special case. QS measures the conditional quantile loss of the tail risk and inherits the merits of MS. We construct an estimator of the QS and establish the asymptotic normality behavior of the estimator. Our simulation shows that the newly proposed measures compare favorably in robustness with other widely used measures such as ES and VaR. 相似文献
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In this study, a new nonparametric approach using Bernstein copula approximation is proposed to estimate Pickands dependence function. New data points obtained with Bernstein copula approximation serve to estimate the unknown Pickands dependence function. Kernel regression method is then used to derive an intrinsic estimator satisfying the convexity. Some extreme-value copula models are used to measure the performance of the estimator by a comprehensive simulation study. Also, a real-data example is illustrated. The proposed Pickands estimator provides a flexible way to have a better fit and has a better performance than the conventional estimators. 相似文献