On the use of inconsistent normalizers for statistical inference on dependent data

Statistical inference, such as confidence interval construction, change point detection and nonparametric regression estimation, has been widely explored in many fields including climate science, economics, finance, industrial engineering and many others. The inference has been well developed in the literature under independent settings, while dependent data, especially time series data, is not uncommon to be observed in these areas. Self-normalization is then proposed to analyze statistical inference for time series data. This thesis first explores asymptotic behavior of optimal weighting in generalized self‑normalization, then proposes self‑normalized simultaneous confidence regions for high‑dimensional time series, and lastly explores unsupervised self‑normalized break test for correlation matrix. The basic idea of self-normalization is that it uses an inconsistent variance estimator as studentizer. The original self-normalizer only considered forward estimators and recently it is generalized to involve both forward and backward estimators with deterministic weights. In the first project, we propose a data-driven weight that corresponds to confidence intervals with minimal lengths and study the asymptotic behavior of such a data-driven weight choice. An interesting dichotomy is found between linear and nonlinear quantities. In the second project, we would like to overcome the dimension limitation of self-normalization and propose a different perspective to make statistical inference of general quantities of high-dimensional time series. Taking the advantage of data with sparse signals, we develop an asymptotic theory on the maximal modulus of self-normalized statistics. We further establish a thresholded self-normalization method to produce simultaneous confidence regions. The method is able to detect uncommon signals among NASDAQ100 in 2016‑2019 in terms of mean and median log returns. In the last project, we move on to unsupervised test for correlation matrix breaks. We develop a self-normalized test tailored to detect correlation matrix breaks. This method is unsupervised and directly compares the estimated correlation before and after the hypothesized change point. We apply the test to the stock log returns of 10 companies and volatility indexes of 5 options on individual equities to show its power of detecting correlation matrix breaks

Less-than-truckload Dynamic Pricing Model in Physical Internet

International audienceThis paper investigates a decision-making problem consisting of less-than-truckload dynamic pricing (LTLDP) under Physical Internet (PI). PI can be seen as the interconnection of logistics networks via open PI-hubs, which can be considered as spot freight markets where LTL requests of different volume/destination continuously arrive over time for a short-stay. Carriers can bid for the requests by using short-term contract. This paper proposes a dynamic pricing model to optimise carrier’s bid price to maximise his expected profits. Three influencing factors are investigated: requests quantity, carrier’s capacity and cost. The results provide useful guidelines to carriers on pricing decisions in PI-hub

Reading Scene Text in Deep Convolutional Sequences

We develop a Deep-Text Recurrent Network (DTRN) that regards scene text reading as a sequence labelling problem. We leverage recent advances of deep convolutional neural networks to generate an ordered high-level sequence from a whole word image, avoiding the difficult character segmentation problem. Then a deep recurrent model, building on long short-term memory (LSTM), is developed to robustly recognize the generated CNN sequences, departing from most existing approaches recognising each character independently. Our model has a number of appealing properties in comparison to existing scene text recognition methods: (i) It can recognise highly ambiguous words by leveraging meaningful context information, allowing it to work reliably without either pre- or post-processing; (ii) the deep CNN feature is robust to various image distortions; (iii) it retains the explicit order information in word image, which is essential to discriminate word strings; (iv) the model does not depend on pre-defined dictionary, and it can process unknown words and arbitrary strings. Codes for the DTRN will be available.Comment: To appear in the 13th AAAI Conference on Artificial Intelligence (AAAI-16), 201

A Generalized Two-Component Camassa-Holm System with Complex Nonlinear Terms and Waltzing Peakons

In this paper, we deal with the Cauchy problem for a generalized two-component Camassa-Holm system with waltzing peakons and complex higher-order nonlinear terms. By the classical Friedrichs regularization method and the transport equation theory, the local well-posedness of solutions for the generalized coupled Camassa-Holm system in nonhomogeneous Besov spaces and the critical Besov space B5/22,1×B5/22,1 was obtained. Besides the propagation behaviors of compactly supported solutions, the global existence and precise blow-up mechanism for the strong solutions of this system are determined. In addition to wave breaking, the another one of the most essential property of this equation is the existence of waltzing peakons and multi-peaked solitray was also obtained