Statistical inference for multiple misaligned time series
Bayesian inference for misaligned irregular time series with application to palaeoclimate reconstruction
... m series have their own definition of supports, the number of nugget parameters will increase by the many different ...palaeoclimate inference, upon which other work can be ...the time series ...
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Joint inference of misaligned irregular time series with application to Greenland ice core data
... in time. Multiple cores have different irregularities; and when considered together, they are misaligned in ...lar time series: a data ...of multiple irregular ...the ...
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Statistical Modelling and Inference for Long Gene Expression Time Series
... During the work of this thesis, we have used two different strategies for fitting LME models in parallel. In Chapter 4.2, we did a simulation study based on data from one gene, where the only difference in the two ...
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Statistical inference from atmospheric time series: detecting trends and coherent structures
... Standard statistical methods involve strong as- sumptions that are rarely met in real data, whereas re- sampling methods permit obtaining valid inference without making questionable assumptions about the ...
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Bayesian Inference To Multiple Changes In The Variance Of AR (P) Time Series Model
... Terms: Time series model; Autoregressive model; Variance change; Posterior ...times, inference problems associated with time series models with change point problems are increasingly ...
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Simulating rainfall time-series: how to account for statistical variability at multiple scales?
... at multiple scales is model nesting (Wang and Nathan 2002 ; Srikanthan 2004 , 2005 ; Srikanthan and Pegram 2009 ), which involves the correction of the generated daily rainfall using a multiplicative factor to ...
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Statistical inference of the multiple regression analysis of complex survey data
... any statistical analysis is directly dependent on the correct method used at the analysis ...the statistical analysis, for example linear modeling, of complex sampling (CS) data, otherwise known as ...
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Statistical inference of the time-varying structure of gene-regulation networks.
... of multiple sources of infor- ...the time delay between the phases is very ...of time points, it would be interesting to incorporate a regularization scheme into ARTIVA in order to favour slight ...
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Bayesian inference for nonlinear structural time series models
... September 5, 2012 Abstract This article discusses a partially adapted particle filter for estimating the likelihood of nonlinear structural econometric state space models whose state transition density cannot be ...
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CSI : A nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data
... from time series mRNA expression data are only able to cope with single time series (or single perturbations with biological replicates), it is becoming increasingly common for several ...
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Statistical Inference For Everyone
... the time, or we construct them as we need ...a multiple model comparison, with most of the models with very low prior probabilities that our brain naturally suppresses until ...
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Principles of Statistical Inference
... There are two ways in which probability may be used in statistical discus- sions. The first is phenomenological, to describe in mathematical form the empirical regularities that characterize systems containing ...
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Discrete-Time Stochastic Volatility Models and MCMC-Based Statistical Inference
... Though the standard SV model is able to capture volatility clustering typically exhibited by financial and economic time series, the model implied kurtosis is often far too small to match the sample ...
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Likelihood Inference for Generalized Integer Autoregressive Time Series Models
... Aly, Emad-Eldin A. A., and Nadjib Bouzar. 2019. Expectation thinning operators based on linear fractional probability generating functions. Journal of the Indian Society for Probability and Statistics 20: 89–107. ...
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A flexible approach to parametric inference in nonlinear time series models
... on statistical models which are nonlinear or exhibit structural breaks or time variation in ...or time varying parameter models to examine whether monetary policy rules have changed over ...exhibit ...
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Short Run and Long Run Causality in Time Series: Inference
... of multiple horizon autoregressions of the lag extension technique suggested by Choi (1993) for inference on univariate autoregressive models and by Toda and Yamamoto (1995) and Dolado and Lütkepohl (1996) ...
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Bayesian inference in time series
... Essentially, they share the functional form of the likelihood (the sampling density viewed as a function of the parameters), and combine very naturally with the sample information in exp[r] ...
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Inference on periodicity of circadian time series
... the time series are ...with time. We have also focused on the scenario where groups of replicate time series from different experimental conditions are available to study the hypothesis ...
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Multiple Perspectives on Inference for Two Simple Statistical Scenarios
... 3.3.2. Results and interpretation The Pearson correlation between perceived stress and resting activity in the amygdala is r = 0.555, and the corresponding test yields p = 0.047. Although this is just smaller than the ...
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Multiple perspectives on inference for two simple statistical scenarios
... 3.3.2. Results and interpretation The Pearson correlation between perceived stress and resting activity in the amygdala is r = 0.555, and the corresponding test yields p = 0.047. Although this is just smaller than the ...
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