Relevant bibliographies by topics / Time-series analysis Inference

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Time-series analysis Inference'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Time-series analysis Inference"

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Wiggins, C. H., and I. Nemenman. "Process pathway inference via time series analysis." Experimental Mechanics 43, no. 3 (2003): 361–70. http://dx.doi.org/10.1007/bf02410536.

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Western, Bruce, and Meredith Kleykamp. "A Bayesian Change Point Model for Historical Time Series Analysis." Political Analysis 12, no. 4 (2004): 354–74. http://dx.doi.org/10.1093/pan/mph023.

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Political relationships often vary over time, but standard models ignore temporal variation in regression relationships. We describe a Bayesian model that treats the change point in a time series as a parameter to be estimated. In this model, inference for the regression coefficients reflects prior uncertainty about the location of the change point. Inferences about regression coefficients, unconditional on the change-point location, can be obtained by simulation methods. The model is illustrated in an analysis of real wage growth in 18 OECD countries from 1965–1992.
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Gluhovsky, Alexander, and Ernest Agee. "On the Analysis of Atmospheric and Climatic Time Series." Journal of Applied Meteorology and Climatology 46, no. 7 (2007): 1125–29. http://dx.doi.org/10.1175/jam2512.1.

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Abstract Linear parametric models are commonly assumed and used for unknown data-generating mechanisms. This study demonstrates the value of inferring statistics of meteorological and climatological time series by using a computer-intensive subsampling method that allows one to avoid time series analysis anchored in parametric models with imposed perceived physical assumptions. A first-order autoregressive model, typically adopted as the default model for correlated time series in climate studies, has been selected and altered with a nonlinear component to provide insight into possible errors in estimation due to nonlinearities in the real data-generating mechanism. The nonlinearity undetected by basic diagnostic procedures is shown to invalidate statistical inference based on the linear model, whereas the inference derived through subsampling remains valid. It is argued that subsampling and other resampling methods are preferable in complex dependent-data situations that are typical for atmospheric and climatic series when the real data-generating mechanism is unknown.
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Boretti, Alberto. "Analysis of Segmented Sea level Time Series." Applied Sciences 10, no. 2 (2020): 625. http://dx.doi.org/10.3390/app10020625.

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Records of measurements of sea levels from tide gauges are often “segmented”, i.e., obtained by composing segments originating from the same or different instruments, in the same or different locations, or suffering from other biases that prevent the coupling. A technique is proposed, based on data mining, the application of break-point alignment techniques, and similarity with other segmented and non-segmented records for the same water basin, to quality flag the segmented records. This prevents the inference of incorrect trends for the rate of rise and the acceleration of the sea levels for these segmented records. The technique is applied to the four long-term trend tide gauges of the Indian Ocean, Aden, Karachi, Mumbai, and Fremantle, with three of them segmented.
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Kim, Seong-Eun, Michael K. Behr, Demba Ba, and Emery N. Brown. "State-space multitaper time-frequency analysis." Proceedings of the National Academy of Sciences 115, no. 1 (2017): E5—E14. http://dx.doi.org/10.1073/pnas.1702877115.

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Time series are an important data class that includes recordings ranging from radio emissions, seismic activity, global positioning data, and stock prices to EEG measurements, vital signs, and voice recordings. Rapid growth in sensor and recording technologies is increasing the production of time series data and the importance of rapid, accurate analyses. Time series data are commonly analyzed using time-varying spectral methods to characterize their nonstationary and often oscillatory structure. Current methods provide local estimates of data features. However, they do not offer a statistical inference framework that applies to the entire time series. The important advances that we report are state-space multitaper (SS-MT) methods, which provide a statistical inference framework for time-varying spectral analysis of nonstationary time series. We model nonstationary time series as a sequence of second-order stationary Gaussian processes defined on nonoverlapping intervals. We use a frequency-domain random-walk model to relate the spectral representations of the Gaussian processes across intervals. The SS-MT algorithm efficiently computes spectral updates using parallel 1D complex Kalman filters. An expectation–maximization algorithm computes static and dynamic model parameter estimates. We test the framework in time-varying spectral analyses of simulated time series and EEG recordings from patients receiving general anesthesia. Relative to standard multitaper (MT), SS-MT gave enhanced spectral resolution and noise reduction (>10 dB) and allowed statistical comparisons of spectral properties among arbitrary time series segments. SS-MT also extracts time-domain estimates of signal components. The SS-MT paradigm is a broadly applicable, empirical Bayes’ framework for statistical inference that can help ensure accurate, reproducible findings from nonstationary time series analyses.
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Shellman, Stephen M. "Time Series Intervals and Statistical Inference: The Effects of Temporal Aggregation on Event Data Analysis." Political Analysis 12, no. 1 (2004): 97–104. http://dx.doi.org/10.1093/pan/mpg017.

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While many areas of research in political science draw inferences from temporally aggregated data, rarely have researchers explored how temporal aggregation biases parameter estimates. With some notable exceptions (Freeman 1989, Political Analysis 1:61–98; Alt et al. 2001, Political Analysis 9:21–44; Thomas 2002, “Event Data Analysis and Threats from Temporal Aggregation”) political science studies largely ignore how temporal aggregation affects our inferences. This article expands upon others' work on this issue by assessing the effect of temporal aggregation decisions on vector autoregressive (VAR) parameter estimates, significance levels, Granger causality tests, and impulse response functions. While the study is relevant to all fields in political science, the results directly apply to event data studies of conflict and cooperation. The findings imply that political scientists should be wary of the impact that temporal aggregation has on statistical inference.
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Kořenek, Jakub, and Jaroslav Hlinka. "Causality in Reversed Time Series: Reversed or Conserved?" Entropy 23, no. 8 (2021): 1067. http://dx.doi.org/10.3390/e23081067.

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The inference of causal relations between observable phenomena is paramount across scientific disciplines; however, the means for such enterprise without experimental manipulation are limited. A commonly applied principle is that of the cause preceding and predicting the effect, taking into account other circumstances. Intuitively, when the temporal order of events is reverted, one would expect the cause and effect to apparently switch roles. This was previously demonstrated in bivariate linear systems and used in design of improved causal inference scores, while such behaviour in linear systems has been put in contrast with nonlinear chaotic systems where the inferred causal direction appears unchanged under time reversal. The presented work explores the conditions under which the causal reversal happens—either perfectly, approximately, or not at all—using theoretical analysis, low-dimensional examples, and network simulations, focusing on the simplified yet illustrative linear vector autoregressive process of order one. We start with a theoretical analysis that demonstrates that a perfect coupling reversal under time reversal occurs only under very specific conditions, followed up by constructing low-dimensional examples where indeed the dominant causal direction is even conserved rather than reversed. Finally, simulations of random as well as realistically motivated network coupling patterns from brain and climate show that level of coupling reversal and conservation can be well predicted by asymmetry and anormality indices introduced based on the theoretical analysis of the problem. The consequences for causal inference are discussed.
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Azumah, Karim, Ananda Omutokoh Kube, and Bashiru Imoro Ibn Saeed. "Functional Time Series Analysis of Land Surface Temperature." International Journal of Statistics and Probability 9, no. 5 (2020): 61. http://dx.doi.org/10.5539/ijsp.v9n5p61.

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Parametric modeling imposes rigid assumption on abstraction of physical characteristics of a phenomenon, which in case of model misspecification could give erroneous results. To address the drawbacks, efforts have been channeled on semi-parametric and nonparametric modeling and inference. This study focuses on constructing an estimator and consequently modeling a meteorological temperature time series first by constructing a penalized spline estimator based on cubic splines. The penalized spline estimator proposed, which are known to impose very minimal restrictions on estimation process, provides good fits to observed data with very attractive properties namely consistent as observed in values of the Mean Squared Error from the analysis. The results of our simulations compared favorably with the empirical analysis on average monthly meteorological temperature data obtained from Climate Knowledge Portal World Bank Organization on Ghana for periods 1901-2016.
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Linden, Ariel. "A matching framework to improve causal inference in interrupted time-series analysis." Journal of Evaluation in Clinical Practice 24, no. 2 (2017): 408–15. http://dx.doi.org/10.1111/jep.12874.

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Linden, Ariel. "Using permutation tests to enhance causal inference in interrupted time series analysis." Journal of Evaluation in Clinical Practice 24, no. 3 (2018): 496–501. http://dx.doi.org/10.1111/jep.12899.

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Dissertations / Theses on the topic "Time-series analysis Inference"

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Li, Yang, and 李杨. "Statistical inference for some econometric time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/195984.

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With the increasingly economic activities, people have more and more interest in econometric models. There are two mainstream econometric models which are very popular in recent decades. One is quantile autoregressive (QAR) model which allows varying-coefficients in linear time series and greatly promotes the ranges of regression research. The first topic of this thesis is to focus on the modeling of QAR model. We propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to QAR models, and introduce two valuable quantities, the quantile autocorrelation function (QACF) and the quantile partial autocorrelation function (QPACF). This allows us to extend the Box-Jenkins three-stage procedure (model identification, model parameter estimation, and model diagnostic checking) from classical autoregressive models to quantile autoregressive models. Specifically, the QPACF of an observed time series can be employed to identify the autoregressive order, while the QACF of residuals obtained from the model can be used to assess the model adequacy. We not only demonstrate the asymptotic properties of QCOR, QPCOR, QACF and PQACF, but also show the large sample results of the QAR estimates and the quantile version of the Ljung- Box test. Moreover, we obtain the bootstrap approximations to the distributions of parameter estimators and proposed measures. Simulation studies indicate that the proposed methods perform well in finite samples, and an empirical example is presented to illustrate the usefulness of QAR model. The other important econometric model is autoregressive conditional duration (ACD) model which is developed with the purpose of depicting ultra high frequency (UHF) financial time series data. The second topic of this thesis is designed to incorporate ACD model with one of the extreme value distributions, i.e. Fréchet distribution. We apply the maximum likelihood estimation (MLE) to Fréchet ACD models and derive its generalized residuals for model adequacy checking. It is noteworthy that simulations show a relative greater sensitiveness in the linear parameters to sampling errors. This phenomenon successfully reflects the skewness of the Fréchet distribution and suggests a method to practitioners in proceeding model accuracy. Furthermore, we present the empirical sizes and powers for Box-Pierce, Ljung-Box and modified Box-Pierce statistics as comparisons of the proposed portmanteau statistic. In addition to the Fréchet ACD, we also systematically analyze theWeibull ACD, where the Weibull distribution is the other nonnegative extreme value distribution. The last topic of the thesis explains the estimation and diagnostic checking the Weibull ACD model. By investigating the MLE in this model, there exhibits a slight sensitiveness in linear parameters. However, there is an obvious phenomenon on the trade-off between the skewness of Weibull distribution and the sampling error when the simulations are conducted. Moreover, the asymptotic properties are also studied for the generalized residuals and a goodness-of-fit test is employed to obtain a portmanteau statistic. Through the simulation results in size and power, it shows that Weibull ACD is superior to Fréchet ACD in specifying the wrong model. This is meaningful in practice.<br>published_or_final_version<br>Statistics and Actuarial Science<br>Doctoral<br>Doctor of Philosophy
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Kwok, Sai-man Simon. "Statistical inference of some financial time series models." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36885654.

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黃鎮山 and Chun-shan Wong. "Statistical inference for some nonlinear time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31239444.

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Wong, Chun-shan. "Statistical inference for some nonlinear time series models /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20715316.

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Wang, Chao, and 王超. "Statistical inference for some discrete-valued time series." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48329514.

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Some problems of' statistical inference for discrete-valued time series are investigated in this study. New statistical theories and methods are developed which may aid us in gaining more insight into the understanding of discrete-valued time series data. The first part is concerned with the measurement of the serial dependence of binary time series. In early studies the classical autocorrelation function was used, which, however, may not be an effective and informative means of revealing the dependence feature of a binary time series. Recently, the autopersistence function has been proposed as an alternative to the autocorrelation function for binary time series. The theoretical autopersistence functions and their sample analogues, the autopersistence graphs, are studied within a binary autoregressive model. Some properties of the autopcrsistencc functions and the asymptotic properties of the autopersistence graphs are discussed, justifying that the antopersistence graphs can be used to assess the dependence feature. Besides binary time series, intcger-vall1ed time series arc perhaps the most commonly seen discrete-valued time series. A generalization of the Poisson autoregression model for non-negative integer-valued time series is proposed by imposing an additional threshold structure on the latent mean process of the Poisson autoregression. The geometric ergodicity of the threshold Poisson autoregression with perburbations in the latent mean process and the stochastic stability of the threshold Poisson autoregression are obtained. The maximum likelihood estimator for the parameters is discussed and the conditions for its consistency and asymptotic normally are given as well. Furthermore, there is an increasing need for models of integer-valued time series which can accommodate series with negative observations and dependence structure more complicated than that of an autoregression or a moving average. In this regard, an integer-valued autoregressive moving average process induced by the so-called signed thinning operator is proposed. The first-order model is studied in detail. The conditions for the existence of stationary solution and the existence of finite moments are discussed under general assumptions. Under some further assumptions about the signed thinning operators and the distribution of the innovation, a moment-based estimator for the parameters is proposed, whose consistency and asymptotic normality are also proved. The problem of conducting one-step-ahead forecast is also considered based on hidden Markov chain theory. Simulation studies arc conducted to demonstrate the validity of the theories and methods established above. Real data analysis such as the annual counts of major earthquakes data are also presented to show their potential usefulness in applications.<br>published_or_final_version<br>Statistics and Actuarial Science<br>Doctoral<br>Doctor of Philosophy
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Kwok, Sai-man Simon, and 郭世民. "Statistical inference of some financial time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36885654.

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Lin, Zhongli. "On the statistical inference of some nonlinear time series models." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43757625.

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Kidzinski, Lukasz. "Inference for stationary functional time series: dimension reduction and regression." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209226.

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Les progrès continus dans les techniques du stockage et de la collection des données permettent d'observer et d'enregistrer des processus d’une façon presque continue. Des exemples incluent des données climatiques, des valeurs de transactions financières, des modèles des niveaux de pollution, etc. Pour analyser ces processus, nous avons besoin des outils statistiques appropriés. Une technique très connue est l'analyse de données fonctionnelles (ADF).<p><p>L'objectif principal de ce projet de doctorat est d'analyser la dépendance temporelle de l’ADF. Cette dépendance se produit, par exemple, si les données sont constituées à partir d'un processus en temps continu qui a été découpé en segments, les jours par exemple. Nous sommes alors dans le cadre des séries temporelles fonctionnelles.<p><p>La première partie de la thèse concerne la régression linéaire fonctionnelle, une extension de la régression multivariée. Nous avons découvert une méthode, basé sur les données, pour choisir la dimension de l’estimateur. Contrairement aux résultats existants, cette méthode n’exige pas d'assomptions invérifiables. <p><p>Dans la deuxième partie, on analyse les modèles linéaires fonctionnels dynamiques (MLFD), afin d'étendre les modèles linéaires, déjà reconnu, dans un cadre de la dépendance temporelle. Nous obtenons des estimateurs et des tests statistiques par des méthodes d’analyse harmonique. Nous nous inspirons par des idées de Brillinger qui a étudié ces models dans un contexte d’espaces vectoriels.<br>Doctorat en Sciences<br>info:eu-repo/semantics/nonPublished
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Kwan, Chun-kit. "Statistical inference for some financial time series models with conditional heteroscedasticity." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B39794027.

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Lin, Zhongli, and 林中立. "On the statistical inference of some nonlinear time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43757625.

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Books on the topic "Time-series analysis Inference"

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Robinson, P. M. Autocorrelation-robust inference. Suntory & Toyota International Centres for Economics & Related Disciplines, 1996.

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Gredenhoff, Mikael. Bootstrap inference in time series econometrics. Stockholm School of Economics, EFI, 1998.

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Boswijk, H. Peter. Cointegration, identification, and exogeneity: Inference in structural error correction models. Thesis Publishers, 1992.

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Wensink, Hans Einar. Autoregressive model inference in finite samples =: Autoregressief modelleren op basis van een eindig aantal waarnemingen. Delft University Press, 1996.

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Chevallier, Julien, Stéphane Goutte, David Guerreiro, Sophie Saglio, and Bilel Sanhaji, eds. Financial Mathematics, Volatility And Covariance Modelling: Volume 2. Routledge, 2019.

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Prado, Raquel, and Mike West. Time Series: Modeling, Computation, and Inference. Taylor & Francis Group, 2010.

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Kakizawa, Yoshihide, and Masanobu Taniguchi. Asymptotic Theory of Statistical Inference for Time Series. Springer, 2012.

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Kakizawa, Yoshihide, and Masanobu Taniguchi. Asymptotic Theory of Statistical Inference for Time Series (Springer Series in Statistics). Springer, 2000.

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Methods in Brain Connectivity Inference Through Multivariates Time Series Analysis. Taylor & Francis Group, 2014.

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Sattarhoff, Cristina. Statistical Inference in Multifractal Random Walk Models for Financial Time Series. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2011.

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Book chapters on the topic "Time-series analysis Inference"

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Held, Leonhard, and Daniel Sabanés Bové. "Markov Models for Time Series Analysis." In Likelihood and Bayesian Inference. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-60792-3_10.

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Eichler, Michael. "Causal Inference in Time Series Analysis." In Causality. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119945710.ch22.

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Ryabko, Daniil. "Basic Inference." In Asymptotic Nonparametric Statistical Analysis of Stationary Time Series. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12564-6_3.

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Dickhaus, Thorsten, and Markus Pauly. "Simultaneous Statistical Inference in Dynamic Factor Models." In Time Series Analysis and Forecasting. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28725-6_3.

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Taniguchi, Masanobu, and Yoshihide Kakizawa. "Discriminant Analysis for Stationary Time Series." In Asymptotic Theory of Statistical Inference for Time Series. Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1162-4_7.

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Beran, Jan. "Inference for μ, γ and F." In Mathematical Foundations of Time Series Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-74380-6_9.

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Newbold, P., C. Agiakloglou, and J. Miller. "Long-term inference based on short-term forecasting models." In Developments in Time Series Analysis. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4515-0_2.

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Mayer, N. Michael, Oliver Obst, and Chang Yu-Chen. "Time Series Causality Inference Using Echo State Networks." In Latent Variable Analysis and Signal Separation. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15995-4_35.

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Mills, Terence C. "Statistical Inference, Estimation and Model Building for Stationary Time Series." In The Foundations of Modern Time Series Analysis. Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230305021_9.

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Taniguchi, Masanobu, and Yoshihide Kakizawa. "Statistical Analysis Based on Functionals of Spectra." In Asymptotic Theory of Statistical Inference for Time Series. Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1162-4_6.

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Conference papers on the topic "Time-series analysis Inference"

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Müller, Ursula U., Anton Schick, and Wolfgang Wefelmeyer. "Inference for Alternating Time Series." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0069.

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Caticha, Nestor. "Multigrid Priors for fMRI time series analysis." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2004. http://dx.doi.org/10.1063/1.1835194.

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Gueguen, Lionel, Camille Le Men, and Mihai Datcu. "Analysis of Satellite Image Time Series Based on Information Bottleneck." In Bayesian Inference and Maximum Entropy Methods In Science and Engineering. AIP, 2006. http://dx.doi.org/10.1063/1.2423296.

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Widiputra, Harya. "Evaluation of multivariate transductive neuro-fuzzy inference system for multivariate time-series analysis and modelling." In SIET '20: 5th International Conference on Sustainable Information Engineering and Technology. ACM, 2020. http://dx.doi.org/10.1145/3427423.3427428.

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Asami, Atsushi, Tatsuki Yamada, and Yohei Saika. "Probabilistic inference of environmental factors via time series analysis using mean-field theory of ising model." In 2013 13th International Conference on Control, Automaton and Systems (ICCAS). IEEE, 2013. http://dx.doi.org/10.1109/iccas.2013.6704168.

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Duggento, A., D. G. Luchinsky, V. N. Smelyanskiy, et al. "Applications of dynamical inference to the analysis of noisy biological time series with hidden dynamical variables." In NOISE AND FLUCTUATIONS: 20th International Conference on Noice and Fluctuations (ICNF-2009). AIP, 2009. http://dx.doi.org/10.1063/1.3140527.

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Wang, Yu, Guanqun Cao, Shiwen Mao, and R. M. Nelms. "Analysis of solar generation and weather data in smart grid with simultaneous inference of nonlinear time series." In IEEE INFOCOM 2015 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2015. http://dx.doi.org/10.1109/infcomw.2015.7179451.

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Ceschini, Giuseppe Fabio, Nicolò Gatta, Mauro Venturini, Thomas Hubauer, and Alin Murarasu. "Optimization of Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-63409.

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Statistical parametric methodologies are widely employed in the analysis of time series of gas turbine sensor readings. These methodologies identify outliers as a consequence of excessive deviation from a statistically-based model, derived from available observations. Among parametric techniques, the k-σ methodology demonstrates its effectiveness in the analysis of stationary time series. Furthermore, the simplicity and the clarity of this approach justify its direct application to industry. On the other hand, the k-σ methodology usually proves to be unable to adapt to dynamic time series, since it identifies observations in a transient as outliers. As this limitation is caused by the nature of the methodology itself, two improved approaches are considered in this paper in addition to the standard k-σ methodology. The two proposed methodologies maintain the same rejection rule of the standard k-σ methodology, but differ in the portions of the time series from which statistical parameters (mean and standard deviation) are inferred. The first approach performs statistical inference by considering all observations prior to the current one, which are assumed reliable, plus a forward window containing a specified number of future observations. The second approach proposed in this paper is based on a moving window scheme. Simulated data are used to tune the parameters of the proposed improved methodologies and to prove their effectiveness in adapting to dynamic time series. The moving window approach is found to be the best on simulated data in terms of True Positive Rate (TPR), False Negative Rate (FNR) and False Positive Rate (FPR). Therefore, the performance of the moving window approach is further assessed towards both different simulated scenarios and field data taken on a gas turbine.
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Dundas, R., S. Ouedraogo, and AH Leyland. "OP84 Strengthening the inference from an interrupted time series analysis: evaluation of the effectiveness of health in pregnancy grants in Scotland using routine data." In Society for Social Medicine and Population Health Annual Scientific Meeting 2020, Hosted online by the Society for Social Medicine & Population Health and University of Cambridge Public Health, 9–11 September 2020. BMJ Publishing Group Ltd, 2020. http://dx.doi.org/10.1136/jech-2020-ssmabstracts.83.

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Sparano, Joa˜o V., Eduardo A. Tannuri, Alexandre N. Simos, and Vini´cius L. F. Matos. "On the Estimation of Directional Wave Spectrum Based on Stationary Vessels 1st Order Motions: A New Set of Experimental Results." In ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57431.

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The practicability of estimating directional wave spectra based on a vessel 1st order response has been recently addressed by several researchers. The interest is justified since on-board estimations would only require only a simple set of accelerometers and rate-gyros connected to an ordinary PC. The on-board wave inference based on 1st order motions is therefore an uncomplicated and inexpensive choice for wave estimation if compared to wave buoys and radar systems. The latest works in the field indicate that it is indeed possible to obtain accurate estimations and a Bayesian inference model seems to be the preferable method adopted for performing this task. Nevertheless, most of the previous analysis has been based exclusively on numerical simulations. At Polytechnic School, an extensive research program supported by Petrobras has been conducted since 2000, aiming to evaluate the possibility of estimating wave spectrum on-board offshore systems, like FPSO platforms. In this context, a series of small-scale tests has been performed at the LabOceano wave basin, comprising long and short crested seas. A possible candidate for on-board wave estimation has been recently studied: a crane barge (BGL) used for launching ducts offshore Brazil. The 1:48 model has been subjected to bow and quartering seas with different wave heights and periods and also different levels of directional spreading. A Bayesian inference method was adopted for evaluating the wave spectra based on the time-series of motions and the results were directly compared to the wave spectra measured in the basin by means of an array of wave probes. Very good estimations of the statistical parameters (significant wave height, peak period and mean wave direction) were obtained and, in most cases, even the directional spreading could be properly predicted. Inversion of the mean direction (180° shift), mentioned by some authors as a possible drawback of the Bayesian inference method, was not observed in any case. Sensitivity analysis on errors in the input parameters, such as the vessel inertial characteristics, has also been performed and attested that the method is robust enough to cope well with practical uncertainties. Overall results once again indicate a good performance of the inference method, providing an important additional validation supported by a large set of model tests.
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Reports on the topic "Time-series analysis Inference"

1

Dustafson, Donald E. Adaptive Time Series Analysis Using Predictive Inference and Entropy. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada191858.

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