Area Interaction Point Process with Covariate data
Managing distance and covariate information with point-based clustering
... or point-based measures such as the empty space func- tion, pairwise and nearest neighbour distance ...account covariate and metric bias [3, ...stationary point-patterns for a homo- geneous Poisson ...
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A multi-scale area-interaction model for spatio-temporal point patterns
... simulated data. Additionally, we analyzed a spatio-temporal point pattern of varicella in the city of Valencia, ...exhibit interaction at several scales in space and ...to data that are not ...
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Point process survival models for epilepsy data
... a covariate that is not being considered in the model, and that with the existence of the cure fraction becomes ...simulated data. We simulated a Poisson process with different com- binations of ...
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Continuous inference for aggregated point process data
... Figure 11: Left to Right, illustrating the probability of a Conservative, Labour or Liberal Democrat win. Colour key: [0.0,0.2], (0.2,0.4], (0.4,0.6], (0.6,0.8], (0.8,1.0]. choice of covariates fits best for the ...
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Pairwise interaction point processes for modelling bivariate spatial point patterns in the presence of interaction uncertainty
... pairwise interaction Gibbs marked point process to the data using a Bayesian approach and quantify the inhibitory interactions within and between the two ...
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Spatial point process models for MRI lesion data in multiple sclerosis
... mark process falls short when trying to model individual mark values that are far from the ...large interaction coefficient on the side of the mark process; or, with respect to the Poisson ...
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Marginal Causal Sub-Group Analysis with Incomplete Covariate Data
... missing data process is required for the doubly weighted estimating equation ...observational data setting, it may not be feasible to collect data on the variables that are associated with the ...
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Investigator triangulation in the data interpretation process. An almost untouched research area
... Another area of research in connection with the analysis of interpretation groups might, for example, relate to the structure of interaction in the group and the contextual conditions for this ...
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Point process modeling and estimation: advances in the analysis of dynamic neural spiking data
... the point process modeling framework allows us to examine the effects of multiple model components ...one covariate on the estimated influence of another (Bar-Gad et ...recently, point ...
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The Impact of the Interaction Process on Activities in the Rural Area: A comparison between the cases „Boven Regge‟ and „Azelerbeek‟ in the Netherlands
... the interaction process in the Boven Regge ...starting point compared with the Azelerbeek-case. The starting point of the developments came out of the area itself in the Boven Regge ...
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Analysing mark recapture recovery data in the presence of missing covariate data via multiple imputation
... missing data. If an individual is unobserved at any point within the study, its corresponding covariate value is also ...individual covariate value may still not be ...sheep data that ...
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Covariate models for accident data
... As a statistician with an interest in applications, I wondered how one would in practice fit the models described tonight It seems to me looking at Equation (7) that the ordered transien[r] ...
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Variable selection with incomplete covariate data.
... missing data imputation. To decide on which one fits the data best, either the bivariate normal distribution or the bivariate t-distribution, either criterion AIC or TIC (or their small sample variants) ...
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Discovering Latent Network Structure in Point Process Data
... community process identity model improves predic- tive performance by accounting for higher rates in South and West Chicago where gangs are deeply ...insufficient data to fit all 77 2 potential in- ...More ...
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Generalized score tests for missing covariate data
... CHAPTER VII SUMMARY AND FUTURE RESEARCH 7.1 Summary In this dissertation, we have studied the generalized score tests based on WEE (1.1) with two nuisance functions π and φ for missing covariate data. ...
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An investigation of dynamic covariate effects in survival data
... 6.5 W O SCO PS D ata Summary The West of Scotland Coronary Prevention Study achieved its aim of dem onstrat ing the benefit of pravastatin therapy in the prevention of coronary heart disease events in men with high ...
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Covariate-adjusted measures of discrimination for survival data
... results. Covariate adjustment could be considered for other measures of ...a covariate-specific NRI could be a useful ...which data and whether these different approaches can produce different ...
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Variable Selection for Covariate Dependent Dirichlet Process Mixtures of Regressions
... as base measures. Spike and slabs priors are commonly used in the Bayesian paradigm to perform variable selection and recently they have been employed in non-parametric settings in context of DPM of regressions. We have ...
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Semiparametric estimation of a binary response model with a change-point due to a covariate threshold
... 8 Conclusions This paper has considered the maximum score estimator of a binary response model with a change-point according to the unknown threshold of a covariate, allowing for an arbitrary from of ...
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Nonparametric directionality measures for time series and point process data
... bivariate data to decompose the correlation by ...or point-process (spike train) data or mixtures of the two (hybrid ...train data using simulated cortical neurone networks and ...
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