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Asymptotic nonparametric statistical analysis of stationary time series
Stationarity is a very general, qualitative assumption, that can be assessed on the basis of application specifics. It is thus a rather attractive assumption to base statistical analysis on, especially for problems for which less general qualitative assumptions, such as independence or finite memory, clearly fail. However, it has long been considered too general to be able to make statistical inference. One of the reasons for this is that rates of convergence, even of frequencies to the mean, are not available under this assumption alone. Recently, it has been shown that, while some natural and simple problems, such as homogeneity, are indeed provably impossible to solve if one only assumes that the data is stationary (or stationary ergodic), many others can be solved with rather simple and intuitive algorithms. The latter include clustering and change point estimation among others. In this volume I summarize these results. The emphasis is on asymptotic consistency, since this the strongest property one can obtain assuming stationarity alone. While for most of the problem for which a solution is found this solution is algorithmically realizable, the main objective in this area of research, the objective which is only partially attained, is to understand what is possible and what is not possible to do for stationary time series. The considered problems include homogeneity testing (the so-called two sample problem), clustering with respect to distribution, clustering with respect to independence, change point estimation, identity testing, and the general problem of composite hypotheses testing. For the latter problem, a topological criterion for the existence of a consistent test is presented. In addition, a number of open problems is presented.
Book
Applied Time Series Econometrics
Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. Hence, no textbook has managed to cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. Topics include: unit root and cointegration analysis, structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. Crucial to empirical work is the software that is available for analysis. New methodology is typically only gradually incorporated into existing software packages. Therefore a flexible Java interface has been created, allowing readers to replicate the applications and conduct their own analyses.
eBook
Fake news and science denier attacks on vaccines. What can you do?
Misinformation and disinformation (\"fake news\") about vaccines are contagious-travelling faster and farther than truth. The consequences are serious; leading to negative impacts on health decisions, including vaccine acceptance, and on trust in immunization advice from public health and/or healthcare professional. This article provides a brief overview of evidence-based strategies to address vaccine deniers in public, in clinical practice and in social situations. As well, a strategy to help differentiate between vaccine deniers and simple vaccine refusers in a practice or clinic is provided. Five tactics are widely used by vaccine deniers: conspiracy; fake experts; selectivity; impossible expectations; and misrepresentation and false logic. Recognizing and understanding these tactics can help protect against misinformation and science denialism propaganda. Highlighting the strong medical science consensus on the safety and effectiveness of vaccines also helps. Carefully and wisely choosing what to say and speaking up-whether you are at a dinner party, out with friends or in your medical office or clinic-is crucial. Not speaking up implies you agree with the misinformation. Having healthcare providers recognize and address misinformation using evidence-based strategies is of growing importance as the arrival of the coronavirus disease 2019 (COVID-19) vaccines is expected to further ramp up the vaccine misinformation and disinformation rhetoric. Healthcare providers must prepare themselves and act now to combat the vaccine misinformation tsunami.
Journal Article
Time series analysis for social sciences
\"Time-series, or longitudinal, data are ubiquitous in the social sciences. Unfortunately, analysts often treat the time-series properties of their data as a nuisance rather than a substantively meaningful dynamic process to be modeled and interpreted. Time-Series Analysis for Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time-series econometrics.\"--Provided by publisher.
Book
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing
An innovations state space modeling framework is introduced for forecasting complex seasonal time series such as those with multiple seasonal periods, high-frequency seasonality, non-integer seasonality, and dual-calendar effects. The new framework incorporates Box-Cox transformations, Fourier representations with time varying coefficients, and ARMA error correction. Likelihood evaluation and analytical expressions for point forecasts and interval predictions under the assumption of Gaussian errors are derived, leading to a simple, comprehensive approach to forecasting complex seasonal time series. A key feature of the framework is that it relies on a new method that greatly reduces the computational burden in the maximum likelihood estimation. The modeling framework is useful for a broad range of applications, its versatility being illustrated in three empirical studies. In addition, the proposed trigonometric formulation is presented as a means of decomposing complex seasonal time series, and it is shown that this decomposition leads to the identification and extraction of seasonal components which are otherwise not apparent in the time series plot itself.
Journal Article
Managing immunization stress-related response: A contributor to sustaining trust in vaccines
Adverse events following immunizations (AEFI) are important to identify and manage effectively so as to sustain trust in vaccines and optimize health. The AEFI category related to \"anxiety about the immunization\" was considered problematic as it did not adequately capture the range of stress responses that can occur. The currently used term for this category, immunization stress-related responses (ISRR), is broader, including the full spectrum of signs and symptoms that can arise in response to stress. ISRR can include vasovagal reactions (fainting), hyperventilation and functional neurological symptoms (e.g. weakness, nonepileptic seizures). It is based on a biopsychosocial framework in which biological (e.g. age, sex), psychological (e.g. preparedness, previous experiences, anxiety) and social factors (e.g. response by others, social media) interact to create an individual's stress response to the immunization process. New guidance is available on prevention, early detection and management of ISRRs which is summarized in the article.Adverse events following immunizations (AEFI) are important to identify and manage effectively so as to sustain trust in vaccines and optimize health. The AEFI category related to \"anxiety about the immunization\" was considered problematic as it did not adequately capture the range of stress responses that can occur. The currently used term for this category, immunization stress-related responses (ISRR), is broader, including the full spectrum of signs and symptoms that can arise in response to stress. ISRR can include vasovagal reactions (fainting), hyperventilation and functional neurological symptoms (e.g. weakness, nonepileptic seizures). It is based on a biopsychosocial framework in which biological (e.g. age, sex), psychological (e.g. preparedness, previous experiences, anxiety) and social factors (e.g. response by others, social media) interact to create an individual's stress response to the immunization process. New guidance is available on prevention, early detection and management of ISRRs which is summarized in the article.
Journal Article
Random time-series model identification from binary-valued observations and quantized measurements
In the paper, two algorithms that allow identification of a parametric models of random time-series from binary-valued observations of their realizations, as well as from quantized measurements of their values, are proposed. The proposed algorithms are based on the idea of time-series decomposition either on a direct power spectral density or autocorrelation function approximation. They use the concepts of randomized search algorithms to recover the corresponding parametric models from calculated estimates of power spectral density or autocorrelation function. The considerations presented in the paper are illustrated with simulated identification examples in which linear and nonlinear block-oriented dynamic models of timeseries are identified from the binary-valued observations and quantized measurements.
Journal Article