A General Quantile Function Model for Economic and Financial Time Series

This article proposed a general quantile function model that covers both one- and multiple-dimensional models and that takes several existing models in the literature as its special cases. This article also developed a new uniform Bayesian framework for quantile function modelling and illustrated the developed approach through different quantile function models. Many distributions are defined explicitly only via their quanitle functions as the corresponding distribution or density functions do not have an explicit mathematical expression. Such distributions are rarely used in economic and financial modelling in practice. The developed methodology makes it more convenient to use these distributions in analyzing economic and financial data. Empirical applications to economic and financial time series and comparisons with other types of models and methods show that the developed method can be very useful in practice

Extreme value prediction via a quantile function model

AbstractMethods for estimating extreme loads are used in design as well as risk assessment. Regression using maximum likelihood or least squares estimation is widely used in a univariate analysis but these methods favour solutions that fit observations in an average sense. Here we describe a new technique for estimating extremes using a quantile function model. A quantile of a distribution is most commonly termed a ‘return level’ in flood risk analysis. The quantile function of a random variable is the inverse function of its distribution function. Quantile function models are different from the conventional regression models, because a quantile function model estimates the quantiles of a variable conditional on some other variables, while a regression model studies the conditional mean of a variable. So quantile function models allow us to study the whole conditional distribution of a variable via its quantile function, whereas conventional regression models represent the average behaviour of a variable.Little work can be found in the literature about prediction from a quantile function model. This paper proposes a prediction method for quantile function models. We also compare different types of statistical models using sea level observations from Venice. Our study shows that quantile function models can be used to estimate directly the relationships between sea condition variables, and also to predict critical quantiles of a sea condition variable conditional on others. Our results show that the proposed quantile function model and the developed prediction method have the potential to be very useful in practice

Simulation of Wave Time Series with a Vector Autoregressive Method

Joint time series of wave height, period and direction are essential input data to computational models which are used to simulate diachronic beach evolution in coastal engineering. However, it is often impractical to collect a large amount of the required input data due to the expense. Based on the nearshore wave records offshore of Littlehampton in Southeast England over the period from 1 September 2003 to 30 June 2016, this paper presents a statistical method to obtain simulated joint time series of wave height, period and direction covering an extended time span of a decade or more. The method is based on a vector auto-regressive moving average algorithm. The simulated times series shows a satisfactory degree of stochastic agreement between original and simulated time series, including average value, marginal distribution, autocorrelation and cross-correlation structure, which are important for Monte Carlo modelling of shoreline evolution, thereby allowing ensemble prediction of shoreline response to a variable wave climate

Neoproterozoic subduction along the Ailaoshan zone, South China : geochronological and geochemical evidence from amphibolite

This study was supported by China Natural Science Foundation (41190073 and 41372198), National Basic Research Program of China (2014CB440901) and Natural Environment Research Council (grant NE/J021822/1).Lenses of amphibolites occur along the Ailaoshan suture zone at the southwestern margin of the Yangtze Block, South China. Petrological, geochemical and zircon U-Pb geochronological data indicate that they are divisible into two coeval groups. Group 1, represented by the Jinping amphibolite, has mg-number of 71-76 and (La/Yb)cn ratios of 7.2-7.7, and displays a geochemical affinity to island arc volcanic rocks. Group 2 amphibolites occur at Yuanyang and are characterized by high Nb contents (14.3-18.4 ppm), resembling Nb-enriched basalts. The epsilon(Nd)(t) values for Group 1 range from -3.45 to -2.04 and for Group 2 from +4.08 to +4.39. A representative sample for Group 1 yields a U-Pb zircon age of 803 7 Ma, whereas two samples for Group 2 give U-Pb zircon ages of 813 +/- 11 Ma and 814 +/- 12 Ma. Petrogenetic analysis suggests that Group 1 originated from an orthopyroxene-rich source and Group 2 from a mantle wedge modified by slab-derived melt. In combination with other geological observations, these amphibolites are inferred to constitute part of an early Neoproterozoic (similar to 815-800 Ma) arc-back-arc basin system. The Neoproterozoic amphibolites and related rocks along the Ailaoshan zone may be the southward extension of the Neoproterozoic supra-subduction zone that developed along the western margin of the Yangtze Block. (C) 2014 Elsevier B.V. All rights reserved.PostprintPeer reviewe

Neoproterozoic crustal growth of the Southern Yangtze Block : Geochemical and zircon U–Pb geochronological and Lu-Hf isotopic evidence of Neoproterozoic diorite from the Ailaoshan zone

This study was supported by National Natural Science Foundation of China (41190073 and 41372198), National Basic Research Program of China (2014CB440901), Basic Operation Expense of Sun Yat-Sen University and Startup Foundation for Doctors of Guilin University of Technology (002401003475).Abstract Neoproterozoic felsic igneous rocks associated with mafic-ultramafic bodies along the margins of the Yangtze Block, South China, can be used to constrain the continental crustal growth and secular evolution of the region. LA-ICPMS zircon U-Pb dating of the Adebo quartz diorite pluton in the Ailaoshan tectonic zone on the southern margin of the Yangtze Block gives the Neoproterozoic age of 800 ± 7 Ma and ɛHf(t) values in the range of -1.03 to +3.75 with two-stage model age of 1.3-1.6 Ga. The pluton is characterized by relatively low SiO2 (60.97-64.41 wt. %) and total alkalis (K2O + Na2O, 7.35-9.14 wt. %) and high Al2O3 content (16.98-18.21 wt. %) with mg-number of 36-39. REE-normalized patterns show enrichment in LREE with (La/Yb)cn of 11.36 to 19.77 and Europium negative anomalies with Eu/Eu* = 0.61- 0.74. The samples are characterized by negative Nb-Ta ((Nb/La)n = 0.18-0.35) and P, Ti, Sr anomalies and high Y concentrations (33.79-41.04 ppm) and low Sr/Y ratios (5.65-10.16). Their isotopic composition are similar to those of the Neoproterozoic mafic igneous rocks in the Ailaoshan zone and the southwestern Yangtze Block, indicating that the quartz diorite was produced by partial melting of mafic lower crust. The diorite also shows the similar geochemical characteristics with adakitic rocks from thickened lower crust or amphibolite and eclogite experimental melts. In combination with their arc-related geochemical signatures and synchronous developed adakitic rocks in the region, the Adebo quartz diorite pluton might be produced in a subduction-related tectonic setting during Neoproterozoic crustal growth along the margins of Yangtze Block.PostprintPeer reviewe

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance. Our code is available at https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201

A simulation method for finite non-stationary time series

When uploading content they are required to comply with their publisher agreement and the SHERPA RoMEO database to judge whether or not it is copyright safe to add this version of the paper to this repository. Abstract In this paper we propose a novel simulation method which enables us to obtain a large number of simulated time series cheaply. The developed method can be applied to any non-stationary time series of finite length and it guarantees that not only the marginal distributions but also the autocorrelation structures of observed and simulated time series are the same. Extensive simulation studies have been conducted to check the performance of our method and to assess if the overall dynamics of the observed time series is preserved by the simulated realizations. The developed simulation method has also been applied to the real size data of cocoon filament, which can be reeled from a cocoon produced by a silkworm. Very good results have been achieved in all the cases considered in the paper