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1.
在加速寿命试验的可靠性设计中, 随机化设计的限制以及删失数据不可避免地导致低分位数估计出现较大的偏差。针对上述的问题, 结合贝叶斯抽样技术以及非线性混合模型(nonlinear mixed model, NLMM)提出了一种可靠性改进的分析方法。首先, 需要检验所收集的数据是否服从威布尔分布以及验证形状参数是否是恒定常数。其次, 考虑随机效应对尺度参数和形状参数的影响, 运用NLMM构建了尺度参数和形状参数与试验因子之间的函数关系。然后, 利用贝叶斯方法估计低分位数的可靠性寿命。最后, 实际案例研究表明, 在考虑删失问题和未完全随机设计的影响时, 所提方法能够获得更为稳健和可靠的估计结果。 相似文献
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在传统的风险度量方法中,常见的协方差估计量并未区分资产收益的下侧风险和上侧收益,而一般的下偏矩估计量则存在非对称性和难以加总的缺点.本文引入已实现半协方差矩阵(RSCOV)作为风险度量进行波动率预测和投资组合研究.本文将RSCOV应用于两种常见的风险分散投资策略—风险平价(ERC)策略和全局方差最小(GMV)策略,并将机器学习中的在线加权集成(OWE)算法用于提升已实现波动率预测方法HAR-RV的样本外预测表现.通过研究发现,相比起已有的其他风险衡量方式,仅包含负向波动信息的下半RSCOV能够更好地被用于平衡组内各资产的风险贡献.基于A股市场2011-2018年的高频数据,本文通过实证研究发现,OWE-HARRV在月度预测步长下的效果优于HAR-RV,而下半RSCOV则能够使ERC策略以及GMV策略在保证一定平均收益的同时,降低了组合收益的极端损失. 相似文献
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Matthew L. Buffington Massimo Giorgini Chia-Hua Lue Giorgio Formisano Pasquale Cascone Mattias Forshage 《Journal of Natural History》2020,54(9-12):565-583
ABSTRACT In the search for native Asian parasitoids of Drosophila suzukii, the notorious spotted-wing Drosophila (SWD), an odd new species of Eucoilinae was discovered. Leptopilina lasallei sp. nov. is herein described and diagnosed relative to other eucoilines associated with drosophilid hosts. Morphologically, L. lasallei is somewhat aberrant within Leptopilina; phylogenetically, L. lasallei is sister group to the core Leptopilina. In the process of investigating L. lasallei, a de novo molecular phylogeny of Leptopilina was generated and is included here. The integrated approach used for the characterisation of L. lasallei, and the resulting phylogeny of Leptopilina, produced data useful to select parasitoid species for SWD biological control.http://www.zoobank.org/urn:lsid:zoobank.org:act:402D504A-4616-4524-85D7-1C13A6276F06 http://www.zoobank.org/urn:lsid:zoobank.org:act:402D504A-4616-4524-85D7-1C13A6276F06 相似文献
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利用φ混合序列矩不等式和截尾的处理方法,研究非同分布φ混合序列加权和强极限收敛性质的问题,得到了若干新结果,推广并改进了独立同分布情形下的相应结果. 相似文献
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This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions. 相似文献
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Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States 下载免费PDF全文
Gabriele Di Filippo 《Journal of forecasting》2015,34(8):619-648
The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
10.
Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle 下载免费PDF全文
Travis J. Berge 《Journal of forecasting》2015,34(6):455-471
Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献