[PDF] Top 20 Point process survival models for epilepsy data
Has 10000 "Point process survival models for epilepsy data" found on our website. Below are the top 20 most common "Point process survival models for epilepsy data".
Point process survival models for epilepsy data
... the epilepsy types, both Tonic-Clonic only and Second generalized Tonic-Clonic, have higher estimated coefficients for the truncated joint ...the epilepsy syndrome on the homogene- ity level on the ...the ... See full document
207
Markov chain Monte Carlo methods for state space models with point process observations
... complete data likelihood (also known as the Q-function) was unimodal and highly nongaussian (skewed) with respect to its param- eters (Yuan & Niranjan, ... See full document
26
Point And Density Forecasts In Panel Data Models
... panel data model and use Tweedie's formula to construct an approximation to the posterior mean of the heterogeneous regression ...Dirichlet process mixture to construct a prior for the distribution of the ... See full document
242
Continuous inference for aggregated point process data
... non-Markovian models for spatial correlation in the field Y ; the extension of our methods to Markov models or low-rank models of spatial/spatiotemporal correlation is ... See full document
33
Change Point Analysis of Survival Data with Application in Clinical Trials
... rate models [5] [6] [8] [15] with unknown pre-change and post-change ...change point in presence of nuisance parameters is reviewed; it appears consistent under certain con- ...the survival function ... See full document
16
Point process models for spatio temporal distance sampling data from a large scale survey of blue whales
... the process generating distance sampling data as a thinned spatial point process and propose model-based inference using a spatial log-Gaussian Cox ...sity models and permits prediction ... See full document
28
Bayesian modelling of integrated data and its application to seabird populations
... climate models for this variable are ...in survival (Crespin et ...for survival and productivity: the covariates tested were the NAO index, local and regional winter sea surface temperatures, and ... See full document
181
Flexible Partially Linear Single Index Regression Models for Multivariate Survival Data
... For data with a high censoring rate, one useful way to choose the cut points is based on the Kaplan-Meier survival probability without considering the covariate ...same survival probability in each ... See full document
118
Nonparametric directionality measures for time series and point process data
... autoregressive models to describe the pattern of interactions be- tween the ...neurophysiological data sets (Baccala et ...series data. (Thomson and Chave, 1991) suggested that AR models are ... See full document
26
Flexible linear mixed models with improper priors for longitudinal and survival data
... heart data support dependence between the ran- dom effect components with normal random slope and skew-normal random ...HIV data present clear evidence for heavy-tailed random ...HIV data than those ... See full document
30
Analyse problems, not data:One world, one health
... parametric models for spatial lattice data and spatial point process data, respectively, but were forced to use ad hoc methods for fitting the models to ... See full document
6
Point process based models for rainfall.
... rainfall data, where the states correspond to periods of rain below and above a certain ...the process, and the other where the mean depend stochastically on the state ...the data, but this is not a ... See full document
135
Transformation Models for Survival Data Analysis with Applications
... transformation models can be applied to a variety of survival ...cure models are motivated from clinical trials where the end point is not death, such as relapse-free survival time, it ... See full document
23
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 ...mark process; or, with respect to the Poisson point process, large local values in the intensity, ... See full document
183
Probabilistic Typology: Deep Generative Models of Vowel Inventories
... our data: it is able to learn not only which vowels are popular among languages, but also which IPA symbols are popular or conventional among the descriptive phoneticists who created our dataset (see footnote 6), ... See full document
11
Survival models for censored point processes
... Hougaard 1987 gives a good overview of the analysis of multivariate survival data, and also discusses some aspects of recurrent event data in the form of counts, and Poisson mixture mode[r] ... See full document
239
A Structured Process for Transforming Usability Data into Usability Information
... usability data generated by these ...usability data into usability information is relatively ...structured process for transforming raw usability data into usability information that is based ... See full document
17
Generalised linear models for flexible parametric modelling of the hazard function
... standard survival models all provided implausible ...identify models which simultaneously provide good within-sample fit and plausible ...observed data relate to the external ... See full document
22
Point process modelling of coordinate based meta analysis neuroimaging data
... The first step in a meta-analysis is a literature search [Normand, 1999]. During this step investigators use databases to retrieve all previous work which is relevant to the question of interest. Ideally, this ... See full document
182
Use of Bayesian Mixture Models in Analyzing Heterogeneous Survival Data: A Simulation Study
... Survival studies consist of several statistical methods in which the response variable is time to occurrence of an event and is widely used in medicine, economics, biology, and social sciences(1). The main ... See full document
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