Going Off the Grid: Iterative Model Selection for Biclustered Matrix Completion

We consider the problem of performing matrix completion with side information on row-by-row and column-by-column similarities. We build upon recent proposals for matrix estimation with smoothness constraints with respect to row and column graphs. We present a novel iterative procedure for directly minimizing an information criterion to select an appropriate amount of row and column smoothing, namely, to perform model selection. We also discuss how to exploit the special structure of the problem to scale up the estimation and model selection procedure via the Hutchinson estimator, combined with a stochastic Quasi-Newton approach. Supplementary material for this article is available online.     

论双系统估计量的无偏性

人口普查质量评估中所使用的双系统估计量是否为无偏估计量是一个很值得深入讨论的问题。只有无偏,才能确保使用双系统估计量估计的目标总体实际人数及人口普查净误差平均等于它们的实际数。针对人口普查质量评估工作中所使用的双系统估计量,论证这个估计量的无偏性条件。采用从既定假设出发进行推演的路径论证。研究结果表明,双系统估计量是目标总体实际人数无偏估计量的必要但非充分的条件是,人口普查与其质量评估调查相互独立以及目标总体中的每一个人在人口普查中的登记概率相同,在质量评估调查中登记的概率也相同。     

交互作用偏差对双系统估计量的影响

虽然双系统估计量目前是人口普查质量评估领域估计总体实际人口数的主要方法,但其内在固有的缺陷却依然存在,即由于人口普查与其质量评估调查不独立引起的交互作用偏差使其低估或高估人口数。独立性假设失败源于在普查及其质量评估调查中登记的因果相关性,以及在普查及质量评估调查中登记概率的异质性。Bell模型是当前公认的测算交互作用偏差的有效方法。该方法建立在0-17岁及成年女性的双系统估计值不存在交互作用偏差的假设条件下。利用美国普查局提供的2010年资料全面展示了双系统估计量交互作用偏差的测算过程。实证结果表明,黑人成年男性的双系统估计值存在显著的交互作用偏差。研究有助于我国在未来人口普查质量评估工作中意识到交互作用偏差的存在,把测算的交互作用偏差添加到双系统估计量估计的人口数中,并依据修正后的双系统估计值计算人口普查净误差。     

A posteriori error estimation for the Stokes–Darcy coupled problem on anisotropic discretization

This paper presents an a posteriori error analysis for the stationary Stokes–Darcy coupled problem approximated by finite element methods on anisotropic meshes in or 3. Korn's inequality for piecewise linear vector fields on anisotropic meshes is established and is applied to non‐conforming finite element method. Then the existence and uniqueness of the approximation solution are deduced for non‐conforming case. With the obtained finite element solutions, the error estimators are constructed and based on the residual of model equations plus the stabilization terms. The lower error bound is proved by means of bubble functions and the corresponding anisotropic inverse inequalities. In order to prove the upper error bound, it is vital that an anisotropic mesh corresponds to the anisotropic function under consideration. To measure this correspondence, a so‐called matching function is defined, and its discussion shows it to be useful tool. With its help, the upper error bound is shown by means of the corresponding anisotropic interpolation estimates and a special Helmholtz decomposition in both media. Copyright © 2016 John Wiley & Sons, Ltd.     

Sharper asset ranking from total drawdown durations

The total duration of drawdowns is shown to provide a moment-free, unbiased, efficient and robust estimator of Sharpe ratios both for Gaussian and heavy-tailed price returns. We then use this quantity to infer an analytic expression of the bias of moment-based Sharpe ratio estimators as a function of the return distribution tail exponent. The heterogeneity of tail exponents at any given time among assets implies that our new method yields significantly different asset rankings than those of moment-based methods, especially in periods large volatility. This is fully confirmed by using 20 years of historical data on 3449 liquid US equities.     

Some closed form robust moment‐based estimators for the MEM(1,1)

In this paper, we extend the closed form moment estimator (ordinary MCFE) for the autoregressive conditional duration model given by Lu et al (2016) and propose some closed form robust moment‐based estimators for the multiplicative error model to deal with the additive and innovational outliers. The robustification of the closed form estimator is done by replacing the sample mean and sample autocorrelation with some robust estimators. These estimators are more robust than the quasi‐maximum likelihood estimator (QMLE) often used to estimate this model, and they are easy to implement and do not require the use of any numerical optimization procedure and the choice of initial value. The performance of our proposal in estimating the parameters and forecasting conditional mean μ_t of the MEM(1,1) process is compared with the proposals existing in the literature via Monte Carlo experiments, and the results of these experiments show that our proposal outperforms the ordinary MCFE, QMLE, and least absolute deviation estimator in the presence of outliers in general. Finally, we fit the price durations of IBM stock with the robust closed form estimators and the benchmarks and analyze their performances in estimating model parameters and forecasting the irregularly spaced intraday Value at Risk.     

Minimum Divergence Estimators,Maximum Likelihood and the Generalized Bootstrap

This paper states that most commonly used minimum divergence estimators are MLEs for suited generalized bootstrapped sampling schemes. Optimality in the sense of Bahadur for associated tests of fit under such sampling is considered.     

Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation

For an industrial process, the estimation of feeding composition is important for analyzing production status and making control decisions. However, random errors or even gross ones inevitably contaminate the actual measurements. Feeding composition is conventionally obtained via discrete and low-rate artificial testing. To address these problems, a feeding composition estimation approach based on data reconciliation procedure is developed. To improve the variable accuracy, a novel robust M-estimator is first proposed. Then, an iterative robust hierarchical data reconciliation and estimation strategy is applied to estimate the feeding composition. The feasibility and effectiveness of the estimation approach are verified on a fluidized bed roaster. The proposed M-estimator showed better overall performance.     

A statistical analysis of power-level-difference-based dual-channel post-filter estimator

By deriving the explicit expression of the probability density function (p.d.f.), this paper presents a statistical analysis of the power-level-difference-based dual-channel post-filter (PLD-DCPF) estimator. The derivation is based on the joint p.d.f. of the auto-spectra of a two-dimensional stationary Gaussian process with mean zero, where the theoretical expression is verified by numerical simulations. Using this theoretical p.d.f. expression, this paper studies the impacts of the correlative parameters on the amount of noise reduction and speech distortion. According to both the theoretical analysis results and the simulation results, four schemes are proposed to improve the performance of the traditional PLD-DCPF estimator.     

The a posteriori error estimates of Chebyshev–Petrov–Galerkin methods for second-order equations

In this paper, the a posteriori error estimates of Chebyshev–Petrov–Galerkin approximations are investigated. For simplicity, we choose the Poisson equation with Dirichlet boundary conditions to discuss the a posteriori error estimators, and deduce their efficient and reliable properties. Some numerical experiments are performed to verify the theoretical analysis for the a posteriori error estimators.