Time

9:15 ~ 9:30am

Opening

Time

9:30 ~ 10:30am

Speaker

Prof. Nourddine Azzaoui (Université Blaise Pascal)

Title

Construction of some  special classes of stable processes that generalizes spatial or temporal Gaussian processes

Abstract

In this talk, we first begin by detailing the motivation behind the construction of these special classes. Indeed, the main incitation is inspired from the second order case where characterizing spatial or temporal processes by a family of sufficient functions, such as via covariance and mean functions,  forms the basis of a large number of statistical modelling approaches. For instance, recently in the active area of Gaussian process regression modelling, the mean and covariance function specify uniquely the properties of the resulting statistical model. In order to allow for non-stationarity, non-independent increments and heavy-tailedness, we present a novel covariation spectral representation of some non-stationary and non-independent increments symmetric a-stable processes (SaS). Such a representation is based on a weaker covariation pseudo additivity condition and should allow a very wide class of statistical regression models to be subsequently developed. In this talk we focus especially on the construction of some examples of these processes; We present some concrete examples that allow  general constructive approaches to building models satisfying these pseudo additivity conditions.

Slides　　　　　Video

Time

10:30 ~ 11:30am

Speaker

Dr. Malcom Egan (Université Blaise Pascal)

Title

Simulation of a general class of stable processes with infinite variance

Abstract

Applications in engineering and finance are driving the development of non-Gaussian models that capture features such as heavy tails and tail dependence. In this talk, we consider one such class of these models based on symmetric alpha-stable processes characterized by bi-additive covariation, which has been introduced in earlier talks by Nourddine Azzaoui and Gareth Peters. In these talks, the process was constructed using the spectral measure representation. In contrast, we focus on the alternative bimeasure representation, where a key challenge is to characterize the class of valid bimeasures. In particular, we show how to construct valid bimeasures and simulate skeletons of the process. We provide some examples to illustrate key features.

Time

13:00 ~ 14:30pm

Speaker

Dr. Gareth W. Peters (UCL)

Title

New Functional and Matrix Variate Regression Approaches for Spatial and Temporal Settings

Abstract

In this talk two new approaches to statistical regression modelling will be discussed. The first involves developing new regression methods for quantile function dynamics under a Symbolic Data Analysis (SDA) formalism. The second approach is a joint temporal panel regression combining dynamic basis function state space models with covariance regressions. New estimation and calibration approaches will be developed. Then two applications of these ideas will be explored, the first in quantile dynamics that link intra-daily volatility modelling with daily volatility. The second example will involve stress testing methodology for multiple yield curve models, with an illustration of the Euro-region Gilts based on credit quality and liquidity factors.

(Slides)*　　　(Video)*　　　*after publishing

Time

14:30 ~ 15:00am

Speaker

Prof. Jennifer S. K. Chan (University of Sydney)

Title

Quantile regression for conditional autoregressive range model

Abstract

To calculate value-at-risk (VaR) for risk management, we derive parametric quantile functions. The general technique is to first build a mean regression model and then estimate families of conditional quantile functions based on the mean regression model. Instead, we propose to regress directly on the quantiles of a distribution and demonstrate the method through the conditional autoregressive range (CARR) model which has increased popularity recently. Two flexible distribution families: the generalized beta type two on positive support and the generalized-t on real support are adopted for demonstration. Then, the models are extended to model the volatility dynamic and compared in terms of goodness-of-fit. The models are implemented using the module fminsearch in Matlab under the classical likelihood approach and applied to analyse the intra-day high-low price ranges from the All Ordinaries index for the Australian stock market to obtain value-at-risk forecasts. VaR are forecast using the proposed models.

Time

15:15 ~ 16:15pm

Speaker

Prof. Mario Wuthrich (ETH Zurich)

Title

Tutorial on Cash Flow Valuation

Abstract

We revisit the theory of cash flow valuation under the paradigm of no-arbitrage. For this purpose we introduce state-price deflators which can be viewed as stochastic discount factors. These state-price deflators provide a consistent valuation framework which leads to a financial market that is free of arbitrage. We describe how these state-price deflators can be constructed, and we show how they are related to equivalent martingale measures and risk neutral pricing. Moreover, we provide several applications including the pricing of zero-coupon bonds, defaultable coupon bonds or NatCat bonds.

(Slides)*　　　(Video)*　　　*after publishing

Time

16:15 ~ 17:15pm

Speaker

Dr. Andrea Macrina (UCL)

Title

Multi-Curve Discounting

Abstract

We present discounting models for the pricing and hedging of financial and insurance instruments in advanced markets and show how the proposed framework extends naturally to asset valuation in emerging (and other developing) markets. The aim is to “catch up” with the requirements of emerging markets when it comes to suitability and usefulness of asset pricing and hedging models, which often have not been developed with the typical properties of emerging markets in mind.

Time

18:20 ~ 21:30pm

Dinner & discussion

Time

8:30 ~ 9:30pm

Speaker

Prof. Yoshiki Yamagata, Dr. Daisuke Murakami (National Institute for Environmental Studies)

Title

Climate change risk management: CO2 and urban heat monitoring & modeling

Abstract

This paper overview the studies regarding the climate change risk managements including CO2 emission reduction and adaptation to climate change impacts. The “Paris Agreement” last year requires countries to do massive emission reduction efforts in the future. However, it is projected that the climate change impact is un avoidable and we need to prepare for the extreme events such as flooding and heat wave risks. Focusing on the GCP related project activities, we report about the new research on the land use scenarios for adapting to flooding risks, global urban carbon mapping and hear risk assessments using rapidly developing big-data. These studies show important opportunities of collaborations between the environmental researchers and the spatial and temporal statistic researchers.

Slides

Time

9:30 ~ 10:30pm

Speaker

Ms. Dorota Toczydlowska (UCL)

Title

Regression Models for Yield Curves

Abstract

In this study we develop a novel class of dimension reduction stochastic multi-factor panel regression based state-space models for the modelling of dynamic yield curves. This includes consideration of Principle Component Analysis (PCA), Independent Component Analysis (ICA) in both vector and function space settings. We embed these rank reduction formulations of multi-curve interest rate dynamics into a stochastic representation for state-space models and compare the results to classical multi-factor dynamic Nelson-Seigal state-space models. We introduce new class of filter to study these models and we also introduce new approaches to the extraction of the independent components via generalisations of the classical ICA methods. This leads to important new representations of yield curve models that can be practically important for addressing questions of financial stress testing and monetary policy interventions. We will illustrate our results on data for multi-curve Libor interest rates in multiple countries.

Slides　　　　　Video

Time

10:30 ~ 11:30am

Speaker

Prof. Guillaume Bagnarosa (ESC Rennes School of Business)

Title

From Local to Global Credit Risk exposure among Farmers

Abstract

We propose in this paper a bottom up model of the credit risk associated to agricultural business taking into consideration the impact of weather conditions on the expected and unexpected losses of a loans portfolio. In order to build and calibrate this model, a large French fertilizer company gives us access to its highly detailed database. Combining these financial and farming data about a set of Romanian farmers with weather and yields information we managed to estimate what we call a local default risk, associated to the region weather conditions uncertainty, and a global default risk, which is rather related to the prices of the internationally traded agricultural commodities.

(This is a joint work with Matthew Ames, Suikai Gao, Donatien Hainaut, Gareth W. Peters and Tomoko Matsui.)

Slides　　　　　Video

Time

11:30 ~ 12:30am

Speaker

Mr. Matthew Ames (UCL)

Title

Oil Futures Price Risk Factors: Short-Term, Medium-Term and Long-Term

Abstract

In this talk, we investigate the influence of observable covariates, such as production, inventory, freight costs, speculative positions etc., on the term structure of oil futures prices. We would like to identify which covariates are significant in explaining oil futures prices in the short-term, the medium-term and the long-term. To this end we explore the following approaches: introducing the instantaneous covariate value into the observation equation; linking the instantaneous covariate value to parameters in the spot price dynamics; exploring lagged covariates. A deeper understanding of the driving covariates behind the dynamics of futures prices is particularly useful for derivatives pricing, real options analysis, risk management and speculative trading.

Slides　　　　　Video

Time

14:30 ~ 15:30pm

Speaker

Prof. Shiyong Zhou (Peking University)

Title

Description of The Earthquake Hazard Insurance Frame in China

Abstract

Slides　　　　　Video

Time

15:30 ~ 16:00pm

Speaker

Prof. Norikazu Ikoma (Nippon Institute of Technology) and Mr. Hiromu Hasegawa (MegaChips Corporation, Japan)

Title

Likelihood with Self-Organizing Map and Gaussian distributions in Hidden Markov Model for Human Moving Behavior Estimation from 3-axis Accelerometer Signal

Abstract

Aimed at practical estimation of human moving behavior by smartphone like appliances with low computational power and small memory size, simple yet efficient methodology using Self-Organizing Map (SOM) and Gaussian distributions as likelihood in Hidden Markov Model has been proposed. Source signal obtained from 3-axis accelerometer has been pre-processed by taking its length followed by Wavelet transform in order to obtain temporal frequency factor and power factor. SOM performs likelihood of the frequency factor while Gaussian distributions perform likelihood of the power factor. Unsupervised learning of SOM generates learned weights of the SOM, which will be used at the other parts’ SOMs in the methodology. Supervised learning with labelled data set provides template output maps of the SOM and Gaussian distributions. The template output maps are masked mean of the SOM output for each category of the labelled data set, where output neurons having large variance have been masked. Taking inner products of the SOM output for current input signal with template SOM outputs provide likelihood values for Hidden Markov Model (HMM), which constrains behavior temporal changes via state transition probabilities. Experiments with real smartphone data during actual commuting and labelled date collected by co-author will show performance of the proposed methodology in comparison with SOM only, SOM and power factor, and in addition with HMM.

Time

16:00 ~ 16:30pm

Speaker

Ms. Xina Cheng  (Waseda University)

Title

Anti-occlusion Observation Model and Automatic Recovery for Multi-view Ball Tracking in Sports Analysis

Abstract

The 3D position of the ball plays a crucial role in professional sport analysis. In ball sports, tracking of ball’s precise position accurately is highly required, whose performance is affected by inaccurate 3D coordinate and occlusion problem. In this paper, we propose anti-occlusion observation model and automatic recovery by 3D ball detection based on multi- view videos to track the ball in 3D space accurately. The anti-occlusion observation model evaluates each camera’s image and eliminates the influence of the camera in which the ball is occluded. The automatic recovery method detects the ball’s 3D position by homography relation of the multi-video and generates a new distribution to initiate the tracker when tracking failure is detected. Experimental results based on the HDTV video sequences, which were captured by four cam- eras located at the corners of the court, show that the success rate of the 3D ball tracking achieves 99.14%.

Slides

Time

17:00 ~ 18:00pm

Speaker

Prof. Arnaud Doucet (University of Oxford)

Title

On pseudo-marginal methods for Bayesian inference in latent

variable models

Abstract

The pseudo-marginal algorithm is a popular variant of the Metropolis-Hastings scheme which allows us to sample asymptotically from a target probability density when we are only able to estimate unbiasedly an un-normalized version of it. It has found numerous applications in Bayesian statistics as there are many latent variable models where the likelihood function is intractable but can be estimated unbiasedly using Monte Carlo samples. In this talk, we will first review the pseudo-marginal algorithm and show that its computational cost is for many common applications quadratic in the number of observations at each iteration. We will then present a simple modification of this methodology which can reduce very substantially this cost. A large sample analysis of this novel pseudo-marginal scheme will be presented.

(This is joint work with George Deligiannidis and Michael K. Pitt)

Slides　　　　　Video

Time

18:00 ~ 19:00pm

Speaker

Prof. Francois Septier (Telecom Lille 1)

Title

Sequential Markov Chain Monte Carlo for Bayesian Filtering with Massive Data

Abstract

Advances in digital sensors, digital data storage and communications have resulted in systems being capable of accumulating large collections of data. In the light of dealing with the challenges that massive data present, I will present in this talk solutions to inference and filtering problems within the Bayesian framework. Two novel Bayesian inference algorithms are developed for non-linear and non-Gaussian state space models, able to deal with large volumes of data (or observations). These are sequential Markov chain Monte Carlo (MCMC) approaches relying on two key ideas: 1) subsample the massive data and utilize a smaller subset for filtering and inference, and 2) a divide and conquer type approach computing local filtering distributions each using a subset of the measurements. Simulation results highlight the accuracy and the large computational savings, that can reach 90% by the proposed algorithms when compared with standard techniques.

(This is a joint work with Allan De Freitas and Lyudmila Mihaylova)

Slides　　　　　Video

Time

19:00 ~ 20:00pm

Speaker

Ms. Anna Zaremba (UCL)

Title

Modelling Causality With Gaussian Process Models

Abstract

In this work we are developing new classes of multivariate time-series models based on Gaussian process functional non-parametric relationships between current vector of observations and the lagged history of each marginal time series. Then within these new classes of non-parameter time series model we consider several families of causality testing that can be used to identify the conditional dependence relationships that may arise in the non-parametric time series structures. This can include structural causality relationships in the mean function, covariance function and higher order information.

   Our main goal is to study the ability to detect causality in such time series models, whilst allowing for very general non-linear causal relationships not often considered in classical parametric time-series models.

   To perform the testing for such features we develop compound tests that first require estimation/calibration of the mean and covariance functions parameterizing the non-parameteric vector valued time series.

   We then provide a generic framework that can be applied to a variety of different problem classes and produce a number of examples to illustrate the ideas developed.

Slides　　　　　Video

Time

9:00 ~ 10:00am

Speaker

Prof. Laurent Clavier (Telecom Lille 1)

Title

Next Generation of wireless communication: some questions concerning reliability

Abstract

In the coming years, for sensing data, a massive number of low-cost, low-energy devices will be deployed in the environment. This significantly modifies the way transmissions have been designed in the last 50 years or more. One crucial question is to have a good representation (model) of what the communication environment will be and especially of the two essential limitation for connectivity: the radio channel and interference. We can then re-visit the fundamental questions (capacity, receiver design) for establishing robust links.

Slides　　　　　Video

Time

10:00 ~ 11:00am

Speaker

Dr. Ido Nevat (Institute for Infocom Research, A-Star)

Title

Query-based Sensors Selection for Collaborative Wireless Sensor Networks with Stochastic Energy Harvesting

Abstract

We develop a statistical decision making framework to choose the optimal sub-set of sensors to activate from a set of all possible sensors, while meeting various Quality of Service (QoS) criteria specified by both the network’s operator as well as user’s queries. The sensor nodes are powered solely by energy harvested from the environment and should be activated in an efficient and economical manner based on the available battery energy, which may not directly observed by the decision maker. Our decision making framework consists of two aspects: estimation of the current available energy of each of the sensors; and a sensor activation policy. To this end, we develop a framework based on ruin theory, where the key metric is maximizing the probability that the network’s lifetime exceeds a fixed time period. We then develop an efficient algorithm to perform the sensor selection which is based on the Cross Entropy method.

Slides　　　　　Video

Time

11:00 ~ 11:30am

Speaker

Prof. Tor Andre Myrvoll (NTNU/SINTEF)

Title

Abstract

Time

13:00 ~ 14:00pm

Speaker

Prof. Kazuya Takeda(Nagoya University)

Title

Abstract

Slides　　　　　Video

Time

14:00 ~ 14:30pm

Speaker

Prof. Jiancang Zhuang (ISM) and Prof. Jorge Mateau (University Jaume I of Castellon)

Title

Semi-parametric estimates of the long-term background trend, periodicity, and clustering effect in crime data

Abstract

Past studies have shown that crime behaviors are clustered. This study proposes a spatiotemporal Hawkes-type point-process model, which includes a background component with daily and weekly periodization and a clustering component that is triggered by previous events, for describing the occurrences of violence or robbery related to crimes in the city of Castellon, Spain, during 2012 and 2013. A nonparametric method, called stochastic reconstruction, is used to estimate each component, including daily and weekly periodicity of background rate, spatial background rate, long-term background trend, and the spatial and temporal response function in the triggering component, of the conditional intensity of the model. The results show that about 3 percents of the crimes can be explained by clustering.

Keywords:

spatiotemporal point process; Hawkes process; stochastic reconstruction; edge effect correction

Slides　　　　　Video

Time

14:30 ~ 15:30pm

Speaker

Prof. Kenji Fukumizu (ISM)

Title

Kernel Methods for Topdogical Data Anaiysis

Abstract

Slides

Time

16:00 ~ 17:00pm

Speaker

Prof. Taiji Suzuki (Tokyo Institute of Technology)

Title

Statistical Performance and Computational Efficiency of Nonparametric Low Rank Tensor Estimators

Abstract

In this talk, we consider statistical performances of some nonparametric tensor estimators. Nonparametric tensor models have a wide range of applications such as recommendation system, spatio-temporal data analysis, and multi-task learning. We show that a Bayesian approach achieves the mini-max optimal predictive accuracy. On the other hand, an alternating minimization approach has a computationally attractive property. That is, the method converges linearly to a good local solution that is mini-max optimal if we assume stronger conditions than the Bayes estimator. We discuss the trade-off between the statistical performances and the computational efficiency by showing some theoretical and numerical results.

Slides　　　　　Video

Time

17:00 ~ 18:00pm

Speaker

Prof. Jen-Tzung Chien (National Chiao Tung University)

Title

Neural Network Learning

Abstract

In this talk, I will present a variety of learning strategies to deal with different issues in neural network model. In tensor factorized neural network, a tensor factorized error backpropagation algorithm is developed to preserve the structure of tensor inputs in layer-wise network during training a regression or classification network. We further present a semi-supervised learning for domain adaptation based on neural network model which jointly minimizes the divergence between the distributions from labeled and unlabeled data in source and target domains, the reconstruction errors due to an auto-encoder, and the classification errors due to the labeled data. Finally, a Bayesian unfolding inference is proposed to integrate the benefits from model-based method and neural network model. A Bayesian unfolded topic model is proposed to improve traditional topic model based on variational inference. A number of applications and future works will be addressed.

Slides

Time

18:00 ~ 18:30pm

Speaker

Prof. Konstatin Markov (Aizu University), Prof. Tomoko Matsui (ISM)

Title

Articulatory and Spectrum Features Integration Using Generalized Distillation Framework

Abstract

It has been shown that by combining the acoustic and articulatory information significant performance improvements in automatic speech recognition(ASR) task can be achieved. In practice, however, articulatory information is not available during recognition and the general approach is to estimate it from the acoustic signal. In this paper, we propose different approach based on the generalized distillation framework, where acoustic-articulatory inversion is not necessary. In this framework, it is assumed that during training, in addition to the training data, some kind of "privileged" information is available and can be used to guide the training process. This allows to obtain a system which at test time outperforms those built on regular training data alone. Our system uses hybrid DNN-HMM acoustic model where neural networks provide HMM state probabilities during decoding. We trained two DNN models: one called "teacher" learns from both acoustic and articulatory features, and the other one called "student" is trained on acoustic features only, but its training process is guided by the "teacher" model. For experiments, we used the Wisconsin X-ray Microbeam Database (XRMB) which consists of parallel articulatory and acoustic data. Results clearly show that distillation framework is effective and allows to achieve significant reduction in the phoneme error rate.

Slides　　　　　Video

Time

19:20 ~ 22:30pm

Dinner & discussion

Time

9:00 ~ 10:00am

Speaker

Prof. Hiroshi Nakagawa (University of Tokyo)

Title

Privacy Protection：Overview

Abstract

Privacy protection is a big issue for utilizing big data including various personal data. In addition to rapid growth of information technologies, regulations on personal data are changing recently. For instance, 14 April 2016, General Data Protection Regulations is adopted by the European Parliament.  In Japan, Personal Information Protection Act is approved Sep. 2015. To cope with or even utilize these circumstances, the overview of mathematical models of privacy protection is described from the viewpoint of what is to be protected for what purposes by what method. The topics included are pseudonymization, k-anonymization, questioner’s privacy, differential privacy and so on.

Slides　　　　　Video

Time

10:00 ~ 11:00am

Speaker

Prof. Kana Shimizu (Waseda University)

Title

Privacy-preserving genome sequence search

Abstract

The state-of-the-art DNA sequencer generates 160 Giga bases per day, which is hundreds of thousands times as large amount of data as the technology of 15 years ago can generate. The huge cost down in DNA sequencing has encouraged large-scale personal genome sequencing, which eventually spotlighted privacy issues in genomics. In our work, we assumed the frequent case such that the user wishs to query the server while hiding the contents of the query, and developed a novel algorithm that enables searching on DNA sequences without leaking user’s query to the server. The proposed algorithm combines a searchable string data structure such as (positional) Burrows-Wheeler Transform and a cryptographic technique called oblivious transfer, and allows variable length substring match. In an experiment using the dataset created from 1000 Genome project, our algorithm was order of magnitude efficient both in run time and data transfer overhead compared to the base line exhaustive method.

Slides

Time

11:00 ~ 11:30am

Speaker

Prof. Kazuhiro Minami (ISM)

Title

Anonymization and Location Privacy 

Abstract

Location data is useful for various analytic purposes in urban planning and marketing and there is thus a strong demand for its secondary usage.  However, to anonymize time-series location data is notoriously difficult to achieve　due to strong temporal and spatial correlations among data points. In this talk, we introduce a statistical framework for measuring location privacy and discuss possible measures for improving the safety of anonymized location traces.

Slides　　　　　Video

Time

11:30 ~ 12:00am

Speaker

Dr. Toshinao Yoshiba, Dr. Naoshi Tsuchida (Bank of Japan)

Dr. Toshiaki Watanabe (Hitotsubashi University)

Title

The Intraday Market Liquidity of Japanese Government Bond Futures

Abstract

We investigate the intraday market liquidity of the Japanese government bond (JGB) futures. First, we overview the movement of various market liquidity indicators during the past decade, classifying them into four categories: tightness, depth, resiliency, and volume. Second, using the data under the current trade time, we extract their intraday pattern and the autocorrelation. Third, we find that the announcement of economic indicator has a negative effect on these liquidity indicators while the monetary policy announcement and the surprise of economic indicator have a positive effect on volume indicators. Fourth, we show that the shock persistence in liquidity indicators rises around April 2013, and the increased persistence remains in some liquidity indicators even several months after April 2013.

Slides

Time

12:00 ~ 12:15am

Closing