Thomas H. Wonnacott

Bio: Thomas H. Wonnacott is an academic researcher. The author has contributed to research in topics: Bartlett's method. The author has an hindex of 1, co-authored 1 publications receiving 11 citations.

Spectral Analysis Combining a Bartlett Window With an Associated Inner Window

Abstract: Usual methods of spectrum estimation allow very strong frequencies to affect the spectrum estimates at distant frequencies, because the weighting function (spectrum window) cannot be made identical...

Spectrum analysis—A modern perspective

Abstract: A summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented in this tutorial. An examination of the underlying time series model assumed by each technique serves as the common basis for understanding the differences among the various spectrum analysis approaches. Techniques discussed include the classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods. A summary table in the text provides a concise overview for all methods, including key references and appropriate equations for computation of each spectral estimate.

Design of computer simulation experiments for industrial systems

Abstract: Computer simulation experiments design for industrial systems, considering variance techniques, multiple ranking procedures, sequential sampling and spectral analysis

Spectral analysis of data generated by simulation experiments with econometric models

Abstract: This paper is concerned with the use of spectral analysis to analyze data generated by computer simulation experiments with models of economic systems. An example model serves to illustrate two different applications of spectral analysis. First, spectral analysis is used to construct confidence bands and to test hypotheses for the purpose of comparing the results of the use of two or more alternative economic policies. Second, spectral analysis is employed as a technique for validating an econometric model. 1. INTRODUCTION DURING THE PAST decade computer simulation experiments with econometric models have become a commonly employed tool for analyzing the behavior of complex economic systems. While economists have improved the estimation process and have considerably enhanced the descriptive power of their econometric models, there have been fewer imposing gains made in the statistical analysis of the resulting output. The major impetus behind the use of simulation by econometricians and economic policy makers is the possibility (and opportunity) of validating econometric models and testing and evaluating alternative economic policies before they are put into effect on actual economic systems. Complete exploitation of simulation experiments with econometric models implies a thorough analysis of the data so generated. Yet as Burdick and Naylor [5, 31, 32] have pointed out in recent articles, a preoccupation with model building among many econometricians simulating economic systems has unduly diverted attention from experimental

Multiscale analysis of palynological records: new possibilities

Abstract: Palynological records helped to illuminate the past, but we show the take can be made much sharper when statistical analysis recognises the records' scale dependence. The latter is an unavoidable consequence of site selection, sediment sampling, and the samples' arrangement into time series by dating. To make provision for this in statistical analysis, scale has to be incorporated as one of the intrinsic variables. But by incorporating scale, the analysis will render the outcome not to be a single conclusion, the usual case in conventional statistics, but a multitude of conclusions each regarding the same set of response and forcing variables and each as valid at its own scale as any of the other conclusions at theirs. Thus, the central question for a usable Statistics is this: how to incorporate scale into the analysis and still have a unique conclusion. We address the methodological aspects and illustrate them by worked examples. We use 14 sites scattered across the globe. Interestingly, the analysis of...