Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

Risk management is a popular and important problem in academia and industry. From a small-scale system, such as city logistics, to a large-scale system, such as the supply chain of a global industrial or financial system, efficient risk management is required to prevent loss from uncertainty. In this paper, we assume that risk factors follow the Lévy process, and propose a stylized model, based on regression, that can estimate the risk of a complicated system under the framework of nest simulation. Specifically, portfolio risk estimation using the Lévy process is discussed as an example. The stylized model simplifies the risk factors artificially, and provides useful basis functions to fit the portfolio loss with little computational effort. Numerical experiments showed the good performance of the stylized model in estimating risk for the Variance Gamma process and the Normal Inverse Gaussian process, which are two examples of the Lévy process.

https://www.mdpi.com/2227-7390/11/6/1414/pdf?version=1678861285

Stylized Model of Lévy Process in Risk Estimation

We present an integrated model for a single-manufacturer multi-retailer supply chain scenario where the product fits Poisson distribution. The manufacturer orders the required quantity of raw materials, converts them into finished products to deliver to the retailers. An integrated inventory model determines the lot size, taking into the pattern of ordering quantities required by the retailers. The purpose is to maximize the profit from the whole chain. Finally we try to determine the optimal ordering price for each retailer under profit sharing scheme.

An Integrated Model for a Single-Manufacturer Multi-retailer Supply Chain with Poisson Demand

The history of financial market repeated the story of bubble bursting, many investors suffered a lot of loss. Some researches explore the reason behind the bubble. Most of them focus on a single factor instead of a system. In this study, based on analysis on some cases study, a framework about the shadows system behind the bubble is proposed and analyzed.

Analysis of Shadows behind Financial Bubbles

For portfolio value-at-risk analysis, a novel approach is proposed based on Independent Component Analysis (ICA) and Mixture Density Network (MDN). Specifically, the original data is first transformed into separate signals which are independent from each other through ICA. Then using MDN their conditional density functions are fitted, from which the joint distribution function of the multivariate time series could be derived. Finally VaR estimates are calculated based on Monte Carlo simulation. This method successfully circumvents the difficult correlation issue within multivariate time analysis and it achieves superior performance compared to traditional EWMA and MVGARCH techniques in empirical study.

An ICA-MDN Based Multi-stage Model for Portfolio Value-at-Risk Analysis

Collaborative credit granting is a vital link in the operation of AVE chain and its supply chain. This paper first analyzes the measurement model of modern credit risk, and then it puts forth the selection model of collaborative credit-granting guaranty approaches on the basis of capital property pricing, and some corresponding conclusions are further drawn on the empirical studies. At the same time, n- tier expansion is made for the collaborative credit-granting monitoring mechanism on the basis of AVE. Under this mechanism, the credit risk in the AVE enterprises can be optimized; and the AVE chain matches the working mechanism in their capacities of real-time resource sharing, n-tier resource allocation, mission assignment, control and supervision. Ultimately, the distance management and risk blockage could be achieved on the supply chains in the all the AVE enterprises by forming a strong self-organized and self-control working chain.

Kin Keung Lai

Papers

Stylized Model of Lévy Process in Risk Estimation

An Integrated Model for a Single-Manufacturer Multi-retailer Supply Chain with Poisson Demand

Analysis of Shadows behind Financial Bubbles

An ICA-MDN Based Multi-stage Model for Portfolio Value-at-Risk Analysis

Selection model and N-tier expansion of collaborative credit-granting guaranty approaches on the Basis of AVE