Semi-parametric and Nonparametric Inference

半参数和非参数推理

• 批准号: RGPIN-2014-04621
• 负责人: Huang, MeiLing
• 金额: $ 0.8万
• 依托单位: Brock University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636010
• 关键词: Semi; parametric; Nonparametric; Inference

The objective of this research is to develop novel inference methods in two related areas: 1) Inference for heavy tailed distributions; 2) nonparametric inference (for general distributions).1. Inference for Heavy Tailed Distributions Extreme events occur in financial markets, natural disasters, disease control and industrial quality control, which affect human life. It is important but difficult to study these extreme events by using suitable mathematical models. Heavy tailed distributions comprise one class of the extreme value distributions which has been applied widely to risk analysis with applications in economics, industrial engineering, actuarial science, medical research and networks. There are theoretical difficulties in the inference of heavy tailed distributions. The proposed program will explore innovative methods to overcome these difficulties. (1) Heavy tailed data is often complicated. A single distribution may not fit data well. Two new cluster and sieve methods are proposed, which are found to e generally better fitting compared with existing methods. (2) Estimation of a high quantile (Value-at-Risk) of a heavy tailed distribution is an important but difficult problem. Existing inference methods have bias and deficiency problems. A geometric mean method is proposed. Preliminary work shows that the new method yields improvements. Also, I will study estimation for high conditional quantiles (quantile regression). A weighted loss function to produce more accurate predictions is proposed. (3) I will explore innovative approximation methods for heavy tailed distributions, e.g., truncation, level crossing, generalized hyperexponential algorithm, etc. 2. Nonparametric Inference (for general distributions) Nonparametric methods are superior to parametric methods on data sets having complex distributions, e.g. multiple modes. However, compared to parametric methods, nonparametric methods may lack efficiency on tails of the distribution or have technique difficulties. The proposed program will develop novel methods to overcome these problems. (4) A family of weighted empirical distribution functions will be studied. I will explore the optimal weights that minimize the estimation errors to improve tail estimation, by utilizing criteria for measurement of the errors, e.g., Lp-norm, exceedance measure and Hellinger distance. (5) The classical kernel methods have selection problems of bandwidth and kernel. I will develop some non-kernel methods by using Hermite orthogonal series, wavelet and L-statistics to avoid these difficulties and keep good properties.Measurements of the proposed methods (1) to (5) will be: a) Comparing the proposed methods with existing methods on theoretical properties: efficiency, consistency, rate of convergence and robustness; b) Studying Monte Carlo simulations to search for good methods and confirm theoretical results; c) Applying the proposed methods to real problems, to find a best model fitting the data; estimating value-at-risk, survival functions, waiting time in networks and performing goodness of fit tests.Scientific Approach: a) The theoretical approach includes probability, statistical theory, order statistics, stochastic processes, harmonic analysis and approximation theory. b) The computational approach includes: Monte Carlo, bootstrapping, computer programming.Expected Impact: The work will provide an alternative approach for statistical inference. The results are expected to overcome some difficulties in the field, to explore and suggest new ideas for future research, and to benefit Canadian research on extreme values field. This research will contribute to solving real world problems and obtaining accurate solutions.

本研究的目的是在两个相关领域开发新颖的推理方法：1）重尾分布的推理； 2）非参数推理（对于一般分布）。 1．重尾分布的推论金融市场、自然灾害、疾病控制和工业质量控制等领域都会发生极端事件，影响人类的生活。使用合适的数学模型来研究这些极端事件很重要，但也很困难。重尾分布是一类极值分布，已广泛应用于经济学、工业工程、精算学、医学研究和网络中的风险分析。重尾分布的推断存在理论上的困难。拟议的计划将探索克服这些困难的创新方法。 (1) 重尾数据往往比较复杂。单一分布可能无法很好地拟合数据。提出了两种新的聚类和筛选方法，与现有方法相比，它们通常更适合。 （2）重尾分布的高分位数（风险价值）估计是一个重要但困难的问题。现有的推理方法存在偏差和不足的问题。提出了几何平均法。初步工作表明新方法取得了改进。另外，我将研究高条件分位数的估计（分位数回归）。提出了一种加权损失函数来产生更准确的预测。 (3) 我将探索重尾分布的创新逼近方法，例如截断、水平交叉、广义超指数算法等。 2. 非参数推理（对于一般分布） 对于具有复杂分布的数据集，例如非参数方法优于参数方法。多种模式。然而，与参数方法相比，非参数方法可能缺乏分布尾部的效率或存在技术困难。拟议的计划将开发新的方法来克服这些问题。 (4) 将研究一系列加权经验分布函数。我将通过利用误差测量标准（例如 Lp 范数、超越测量和 Hellinger 距离）来探索最小化估计误差的最佳权重，以改进尾部估计。 (5)经典核方法存在带宽和核的选择问题。我将通过使用 Hermite 正交级数、小波和 L 统计来开发一些非核方法，以避免这些困难并保持良好的性能。所提出的方法（1）至（5）的测量将是： a）将所提出的方法与现有方法的理论特性进行比较：效率，一致性，收敛率和鲁棒性； b) 研究蒙特卡罗模拟，寻找好的方法并验证理论结果； c) 将所提出的方法应用于实际问题，找到拟合数据的最佳模型；估计风险价值、生存函数、网络中的等待时间以及执行拟合优度检验。科学方法： a) 理论方法包括概率、统计理论、阶次统计、随机过程、调和分析和近似理论。 b) 计算方法包括：蒙特卡罗、引导、计算机编程。预期影响：这项工作将为统计推断提供另一种方法。研究结果有望克服该领域的一些困难，为未来的研究探索和提出新的思路，并有益于加拿大极值领域的研究。这项研究将有助于解决现实世界的问题并获得准确的解决方案。