الصفحة 2
الصفحة 2
International Competitiveness in Africa : Policy Implications in the Sub-Saharan Region
The effects of international trade and foreign direct investment on developing economies have always been controversial. With the unstoppable spread of globalization and the supremacy of "open" policies over "closed" ones, the debate between "participating" and "not participating" in the world economy has been superseded by discussions on the best policy measures for expanding participation and enhancing the accrued welfare gains. Policies to strengthen international competitiveness are almost unanimously considered important means towards those ends. This book examines two policies frequently used to enhance international competitiveness in Sub-Saharan African economies: exchange rate policy and productivity-related policy.
Innovations in classification, data science, and information systems ; Proceedings of the 27th Annual Conference of the Gesellschaft für Klassifikation e.V., Brandenburg University of Technology, Cottbus, March 12-14, 2003
The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques.
Inference for change point and post change means after a CUSUM test
This monograph is the first to systematically study the bias of estimators and construction of corrected confidence intervals for change-point and post-change parameters after a change is detected by using a CUSUM procedure. Researchers in change-point problems and sequential analysis, time series and dynamic systems, and statistical quality control will find that the methods and techniques are mostly new and can be extended to more general dynamic models where the structural and distributional parameters are monitored. Practitioners, who are interested in applications to quality control, dynamic systems, financial markets, clinical trials and other areas, will benefit from case studies based on data sets from river flow, accident interval, stock prices, and global warming. Readers with an elementary probability and statistics background and some knowledge of CUSUM procedures will be able to understand most results as the material is relatively self-contained.The exponential family distribution is used as the basic model that includes changes in mean, variance, and hazard rate as special cases. There are fundamental differences between the sequential sampling plan and fixed sample size. Although the results are given under the CUSUM procedure, the methods and techniques discussed provide new approaches to deal with inference problems after sequential change-point detection, and they also contribute to the theoretical aspects of sequential analysis. Many results are of independent interests and can be used to study random walk related stochastic models.
Independent component analysis and signal separation ; 7th International Conference, ICA 2007, London, UK, September 9-12, 2007, Proceedings
Independent Component Analysis and Signal Separation has applications at the intersection of many science and engineering disciplinesconcernedwithunderstandingandextractingusefulinformationfrom data as diverse as neuronal activity and brain images, bioinformatics, com- nications, the World Wide Web, audio, video, sensor signals, or time series.
Independent Component Analysis and Blind Signal Separation ; 6th International Conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006, Proceedings
This book constitutes the refereed proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2006, held in Charleston, SC, USA, in March 2006. The 120 revised papers presented were carefully reviewed and selected from 183 submissions. The papers are organized in topical sections on algorithms and architectures, applications, medical applications, speech and signal processing, theory, and visual and sensory processing.
Implementing machine learning for finance : A systematic approach to predictive risk and performance analysis for investment portfolios
Introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios. You will: Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management / Know the concepts of feature engineering, data visualization, and hyperparameter optimization / Design, build, and test supervised and unsupervised ML and DL models / Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices / Structure and optimize an investment portfolio with preeminent asset classes and measure the / underlying risk
Hybrid Intelligent Systems : Analysis and Design
The objective of this edited volume is to offer a general view at the recent conceptual developments of Soft Computing (SC) regarded as a general methodology supporting the design of hybrid systems along with their diversified applications to modeling, simulation and control of non-linear dynamical systems. As of now, SC methodologies embrace neural networks, fuzzy logic, genetic algorithms and chaos theory. Each of these methodologies exhibits well delineated advantages and disadvantages. Interestingly, they have been found useful in solving a broad range of problems. However, many real-world complex problems require a prudent, carefully orchestrated integration of several of these methodologies to fully achieve the required efficiency, accuracy, and interpretability of the solutions. In this edited volume, an overview of SC methodologies, and their applications to modeling, simulation and control, will be given in an introductory paper by the Editors. Then, detailed methods for integrating the different SC methodologies in solving real-world problems will be given in the papers by the other authors in the book. The edited volume will cover a wide spectrum of applications including areas such as: robotic dynamic systems, non-linear plants, manufacturing systems, and time series prediction.
Hilbert-Huang Transform Analysis Of Hydrological And Environmental Time Series
The Hilbert-Huang Transform ((HHT) is a recently developed technique which is used to analyze nonstationary data. Hydrologic and environmental series are, in the main, analyzed by using techniques which were developed for stationary data. This has led to problems of interpretation of the results. Environmental and hydrologic series are quite often nonstationary. The basic objective of the material discussed in this book is to analyze these data by using methods based on the Hilbert-Huang transform. These results are compared to the results from the traditional methods such as those based on Fourier transform and other classical statistical tests.
Heavy-Tailed Time Series
This book aims to present a comprehensive, self-contained, and concise overview of extreme value theory for time series, incorporating the latest research trends alongside classical methodology.Additionally, the book incorporates complete proofs and exercises with solutions as well as substantive reference lists and appendices, featuring a novel commentary on the theory of vague convergence.
Heavy-Tail Phenomena : Probabilistic and Statistical Modeling
This comprehensive text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. Heavy tails are characteristic of phenomena where there is a significant probability of a single huge value impacting system behavior. Record-breaking insurance losses, financial returns, sizes of files stored on a server, transmission rates of files are all examples of heavy-tailed phenomena.
Handbook of Marketing Decision Models
The HANDBOOK OF MARKETING DECISION MODELS presents the state of the art in marketing decision models, dealing with new modeling areas such as customer relationship management, customer value and online marketing, but also describes recent developments in other areas. In the category of marketing mix models, the latest models for advertising, sales promotions, sales management, and competition are dealt with. New developments are presented in consumer decision models, models for return on marketing, marketing management support systems, and in special techniques such as time series and neural nets. Not only are the most recent models discussed, but the book also pays attention to the implementation of marketing models in companies and to applications in specific industries.
Fundamentals of statistics with fuzzy data
This research monograph presents basic foundational aspects for a theory of statistics with fuzzy data, together with a set of practical applications. Fuzzy data are modeled as observations from random fuzzy sets. Theories of fuzzy logic and of random closed sets are used as basic ingredients in building statistical concepts and procedures in the context of imprecise data, including coarse data analysis. The monograph also aims at motivating statisticians to look at fuzzy statistics to enlarge the domain of applicability of statistics in general.
Functional and operatorial statistics
An increasing number of statistical problems and methods involve infinite-dimensional aspects. This is due to the progress of technologies which allow us to store more and more information while modern instruments are able to collect data much more effectively due to their increasingly sophisticated design. This evolution directly concerns statisticians, who have to propose new methodologies while taking into account such high-dimensional data (e.g. continuous processes, functional data, etc.). The numerous applications (micro-arrays, paleo-ecological data, radar waveforms, spectrometric curves, speech recognition, continuous time series, 3-D images, etc.) in various fields (biology, econometrics, environmetrics, the food industry, medical sciences, paper industry, etc.) make researching this statistical topic very worthwhile. This book gathers important contributions on the functional and operatorial statistics fields
Frontiers in Statistical Quality Control 8
The proportion of nonconforming meters in a lot has traditionally defined lot quality for utility meter sampling inspection purposes. However, lot quality is usually measured on the basis of two criteria for such products: the proportion of nonc- forming packages in the lot and the lot mean quantity.
Forecasting with Exponential Smoothing : The State Space Approach
Exponential smoothing methods have been around since the 1950s, and are the most popular forecasting methods used in business and industry. Recently, exponential smoothing has been revolutionized with the introduction of a complete modeling framework incorporating innovations state space models, likelihood calculation, prediction intervals and procedures for model selection. In this book, all of the important results for this framework are brought together in a coherent manner with consistent notation. In addition, many new results and extensions are introduced and several application areas are examined in detail.
Food Price Volatility and Its Implications for Food Security and Policy
This book provides fresh insights into concepts, methods and new research findings on the causes of excessive food price volatility. It also discusses the implications for food security and policy responses to mitigate excessive volatility. The approaches applied by the contributors range from on-the-ground surveys, to panel econometrics and innovative high-frequency time series analysis as well as computational economics methods. It offers policy analysts and decision-makers guidance on dealing with extreme volatility.
Finite Mixture and Markov Switching Models
The prominence of finite mixture modelling is greater than ever. Many important statistical topics like clustering data, outlier treatment, or dealing with unobserved heterogeneity involve finite mixture models in some way or other. The area of potential applications goes beyond simple data analysis and extends to regression analysis and to non-linear time series analysis using Markov switching models.It is the first time that the Bayesian perspective of finite mixture modelling is systematically presented in book form. It is argued that the Bayesian approach provides much insight in this context and is easily implemented in practice. Although the main focus is on Bayesian inference, the author reviews several frequentist techniques, especially selecting the number of components of a finite mixture model, and discusses some of their shortcomings compared to the Bayesian approach. The aim of this book is to impart the finite mixture and Markov switching approach to statistical modelling to a wide-ranging community. This includes not only statisticians, but also biologists, economists, engineers, financial agents, market researcher, medical researchers or any other frequent user of statistical models. This book should help newcomers to the field to understand how finite mixture and Markov switching models are formulated, what structures they imply on the data, what they could be used for, and how they are estimated.
Financial Modeling Under Non-Gaussian Distributions
Practitioners and researchers who have handled financial market data know that asset returns do not behave according to the bell-shaped curve, associated with the Gaussian or normal distribution. Indeed, the use of Gaussian models when the asset return distributions are not normal could lead to a wrong choice of portfolio, the underestimation of extreme losses or mispriced derivative products. Consequently, non-Gaussian models and models based on processes with jumps are gaining popularity among financial market practitioners.
Experimenting with Dynamic Macromodels : Growth and Cycles
This book presents a macroeconomic dynamic model à la Solow-Swan, including the market for labour, in a discrete time structure. Labour supply is modelled as a reversed S curve (derived in the appendix). The models are expanded to include expenditure on R&D (thus endogenous technical progress), and public expenditure on infrastructures. For each of the three models, numerical simulations are implemented in MAPLE, and the results are shown in time series figures, which make it easy to detect that even small changes in the parameters produce responses in the time behaviour of the main variables: from steady growth, to regular cycles, to chaotic-like time paths. The simulations show that cycles do not promote material welfare, as measured by total undiscounted consumption along the time horizon, and that the comparative action of R&D versus public expenditure is strictly linked to the values assigned to the parameters.
Estimation in Conditionally Heteroscedastic Time Series Models
ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic). This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies.
