New statistical methods and future directions of research in time series

A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include:

• Contributions from eleven of the world’s leading figures in time series
• Shared balance between theory and application
• Exercise series sets
• Many real data examples
• Consistent style and clear, common notation in all contributions
• 60 helpful graphs and tables

Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis.

DNA fingerprinting had a well-defined birthday. In the March 7, 1985 issue of Nature, Alec Jeffreys and coworkers described the first develop ment ofmu1tilocus probes capable of simultaneously revealing hypervari ability at many loci in the human genome and called the procedure DNA fingerprinting. It was a royal birth in the best British tradition. In a few months the emerging technique had permitted the denouement of hith erto insoluble immigration and paternity disputes and was already heralded as a major revolution in forensic sciences. In the next year (October, 1986) DNA fingerprinting made a dramatic entree in criminal investigations with the Enderby murder case, whose story eventually was turned into a best-selling book ("The Blooding" by Joseph Wambaugh). Today DNA typing systems are routinely used in public and commercial forensic laboratories in at least 25 different countries and have replaced conventional protein markers as the methods of choice for solving paternity disputes and criminal cases. Moreover, DNA fingerprinting has emerged as a new domain of intense scientific activity, with myriad applications in just about every imaginable territory of life sciences. The Second International Conference on DNA Fingerprinting, which was held in Belo Horizonte, Brazil in November of 1992, was a clear proof of this."

Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time series An automatic procedure to build univariate ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New methods for finding the number of clusters of time series and discrimination methods, including vector support machines, for time series Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting. Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.

When a young bilingual child experiences language difficulties, it can be hard to tell if those challenges are due to a disorder or just limited exposure to the English language. That's a critical distinction-because while limited language exposure will fix itself over time, a language impairment is a clinical issue that will cause the child difficulties in school without timely and effective intervention. Now there's a valid reliable assessment that specifically responds to the needs of young Spanish-English bilingual children. For use with children ages 4 through 6 years who have varying degrees of bilingualism, the Bilingual English-Spanish Assessment (BESA) was developed to: identify phonological and/or language impairment in bilingual children and English language learners using a standardized protocol differentiate between a delay in English language acquisition and a true language disorder document children's speech and language strengths and needs monitor children's progress in both languages and use the information to make decisions about intervention Through a combination of subtests for students and surveys for teachers and parents, BESA reveals the big picture of a young bilingual child's language development. Comprehensive assessment with BESA includes: 3 subtests (in both English and Spanish). These three standardized, norm-referenced subtests address the key domains of morphosyntax, semantics, and phonology. The subtests are administered with children one-on-one in a quiet, well-lit, well-ventilated room free of distractions. 2 questionnaires. The SLP gathers more information by interviewing both parents and teachers. Bilingual Input-Output Survey (BIOS): determines Spanish and English use at home and school and pinpoints which language(s) need testing Inventory to Assess Language Knowledge (ITALK): identifies the parent's and teacher's perception of the child's performance in each language and highlights areas of possible concern Pragmatic activity: This 5- to 10-minute warmup activity, in which children are asked to "help wrap a present" with the examiner, is used to establish rapport with the child and give clinicians an idea of how collaborative and interactive the child will be during the rest of the assessment. BESA Benefits: Valid and reliable. There are very few assessment tools for bilingual children, and even fewer that are valid and reliable. BESA answers the call for a bilingual assessment backed up with solid evidence of validity and reliability. Culturally and linguistically appropriate. BESA subtests are different for each language; the Spanish subtest isn't just a translation of the English one. Dialects were taken into consideration-the norming sample included 17 Spanish dialects and 7 regional dialects for English-and items were developed for each language based on the markers, structure, and culture of that language. Uncovers the full picture of a child's language development.Not only does BESA identify language impairments, it also helps you gauge progress in both languages and document a child's dominant language across different domains of speech and language. This critical information will help you plan interventions and educational programming for bilingual children. Flexible and convenient. You can use each BESA component independently or combine them as part of an assessment battery. Choose which component you want to give-you're not required to administer both the English and Spanish subtests. BESA can also be given over multiple sessions, so you can easily fit it into your schedule. What's in the kit? 1 BESA Test Manual 1 BESA Stimulus Book 20 BESA Protocol English Forms 20 BESA Protocol Spanish Forms 20 BIOS Forms 20 ITALK forms