On the Inconsistency of Nonparametric Bootstraps for the Subvector Anderson-Rubin Test

Bootstrap procedures based on instrumental variable (IV) estimates or t-statistics are generally invalid when the instruments are weak. The bootstrap may even fail when applied to identification-robust test statistics. For subvector inference based on the Anderson-Rubin (AR) statistic, Wang and Doko Tchatoka (2018) show that the residual bootstrap is inconsistent under weak IVs. In particular, the residual bootstrap depends on certain estimator of structural parameters to generate bootstrap pseudo-data, while the estimator is inconsistent under weak IVs. It is thus tempting to consider nonparametric bootstrap. In this note, under the assumptions of conditional homoskedasticity and one nuisance structural parameter, we investigate the bootstrap consistency for the subvector AR statistic based on the nonparametric i.i.d. bootstap and its recentered version proposed by Hall and Horowitz (1996). We find that both procedures are inconsistent under weak IVs: although able to mimic the weak-identification situation in the data, both procedures result in approximation errors, which leads to the discrepancy between the bootstrap world and the original sample. In particular, both bootstrap tests can be very conservative under weak IVs

Retail Supply Chain Coordination and Collaborative Optimization

The retail industry plays an important role in the economic development of the world. The Collaborative Planning, Forecasting and Replenishment (CPFR) solution can coordinate the business process between the retailers and manufacturers in the retail supply chain and got its applications in many world-renowned retailers around the world. In this paper, CPFR coordination process and its applications will be briefly reviewed at the beginning. And then, an optimization model which can improve performance of retail supply chain coordination is p

A Two-Stage Dynamic Programming Model for Nurse Rostering Problem Under Uncertainty

No abstract provided.Master of Science in EngineeringIndustrial and Manufacturing Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/140733/1/WENJIE WANG_Thesis_Embedded.pdfDescription of WENJIE WANG_Thesis_Embedded.pdf : Thesi

An Improved Sufficient Condition for Routing on the Hypercube with Blocking Nodes

We study the problem of routing between two nodes in a hypercube with blocking nodes using shortest path. This problem has been previously studied by other researchers, they have proposed a few algorithms to solve the problem. Among the work done, one has found several sufficient conditions for such a path to exist. One such condition states that a shortest path between node 0^n and 1^n exists if the number of blocking nodes is less than n in an n-dimensional hypercube. We improve this condition by proposing the condition that if the size of a SDR (system of distinct representatives) for the blocking nodes is less than n, then a shortest path between the two nodes 0^n and 1^n exists. Since the number of blocking nodes can be greater than or equal to n, while the size of SDR is less than n, thus this result improves the previous sufficient condition

Deep Extreme Multi-label Learning

Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves $2^L$ possible label sets especially when the label dimension $L$ is huge, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by originally establishing an explicit label graph. In the meanwhile, deep learning has been widely studied and used in various classification problems including multi-label classification, however it has not been properly introduced to XML, where the label space can be as large as in millions. In this paper, we propose a practical deep embedding method for extreme multi-label classification, which harvests the ideas of non-linear embedding and graph priors-based label space modeling simultaneously. Extensive experiments on public datasets for XML show that our method performs competitive against state-of-the-art result