Search results

Create the page "Joint data models" on this wiki! See also the search results found.

• Modeling the observations
  ...del for categorical data]] | [[Models for time-to-event data ]] | [[Joint models]] ...on, one that evolves with time. As we have decided to work with parametric models, we suppose that there exists a function $\lambda$ such that the distributi
  5 KB (816 words) - 13:46, 25 June 2013
• Overview
  ...ural therefore than to begin this introduction by looking at some observed data. |text=This second example involves weight data for rats measured over 14 weeks, for a sub-chronic [http://en.wikipedia.org
  8 KB (1,231 words) - 10:34, 21 June 2013
• Models
  ===''Continuous data''=== ===''Categorical data''===
  9 KB (1,456 words) - 18:09, 21 January 2013
• Joint models
  ...del for categorical data]] | [[Models for time-to-event data ]] | [[Joint models]] ...dies is to characterize relationships between different types of response data.
  18 KB (2,714 words) - 15:19, 25 June 2013
• Hidden Markov models
  ...]] | [[Hidden Markov models]] | [[Stochastic differential equations based models]] ...in Markov chains] are a useful tool for analyzing categorical longitudinal data. However, sometimes the [https://en.wikipedia.org/wiki/Markov_process Marko
  9 KB (1,350 words) - 15:44, 25 June 2013
• Simulation
  ...hermore, the hierarchical structure of a model allows us to decompose this joint distribution into a product ...$\bu$, individual covariates $\bc$ and population parameters $\theta$, the joint distribution of these random variables can be written:
  7 KB (938 words) - 16:10, 5 June 2013
• Models for count data
  ...del for categorical data]] | [[Models for time-to-event data ]] | [[Joint models]] ...rrhages hemorrhages] or lesions in each given time period. More precisely, data from individual $i$ is the sequence $y_i=(y_{ij},1\leq j \leq n_i)$ where $
  14 KB (2,067 words) - 15:00, 25 June 2013
• Introduction and notation
  ...(log-rate, log-volume, logit-bioavailability, etc.) and now represents the joint distribution of $y_i$ and $\phi_i$: ...i \sim {\cal N}( 0 , \Omega)$. This ''$\eta$-representation'' leads to the joint distribution of $y_i$ and $\eta_i$:
  7 KB (1,027 words) - 14:54, 7 June 2013
• Model for categorical data
  ...del for categorical data]] | [[Models for time-to-event data ]] | [[Joint models]] Assume now that the observed data takes its values in a fixed and finite set of nominal categories $\{c_1, c
  17 KB (2,658 words) - 11:42, 28 August 2013
• Models for time-to-event data
  ...odel for categorical data]] | [[Models for time-to-event data]] | [[Joint models]] These observations can be stored in a data file as shown in the table on the right.
  22 KB (3,174 words) - 15:10, 25 June 2013
• Mixture models
  ...]] | [[Hidden Markov models]] | [[Stochastic differential equations based models]] ...ta is obtained from different individuals from the same population. These models allow us to take into account between-subject variability.
  23 KB (3,513 words) - 15:39, 25 June 2013
• Visualization
  ...ally detect if there are relationships between variables, visually compare data from different groups, etc. Development of such visual exploration tools po illustrate the data visualization part of this chapter, we have created a little Matlab toolbox
  14 KB (2,022 words) - 16:10, 25 June 2013
• Continuous data models
  ...del for categorical data]] | [[Models for time-to-event data ]] | [[Joint models]] == The data ==
  28 KB (4,028 words) - 13:54, 25 June 2013
• The SAEM algorithm for estimating population parameters
  ...likelihood estimator (MLE) in the quite general setting of incomplete data models. SAEM has been shown to be a very powerful NLMEM tool, known to accurately ...\psi_1|y_1),\ldots , \pmacro(\psi_N|y_N)$, using at each step the complete data $(\by,\bpsi^{(k)})$ to calculate a new parameter vector $\theta_k$. We will
  28 KB (4,155 words) - 11:17, 21 June 2013
• What is a model? A joint probability distribution!
  ...resenting the [http://en.wikipedia.org/wiki/Joint_probability_distribution joint distribution] of these random variables. ...of the model will then allow it to be decomposed into submodels, i.e., the joint distribution decomposed into a product of [http://en.wikipedia.org/wiki/Con
  36 KB (5,648 words) - 10:40, 21 June 2013
• Dynamical systems driven by ODEs
  ...[Categorical data models]] | [[Time-to-event data models]] | [[Joint data models]] | [[Dynamical systems driven by ODEs]] :: In this example, the data file contains the viral load.
  23 KB (3,364 words) - 15:16, 4 April 2013
• Estimation
  In the modeling context, we usually assume that we have data that includes observations $\by$, measurement times $\bt$ and possibly a ...f incomplete data models. Indeed, only $\by = (y_{ij})$ is observed in the joint model $\pypsi(\by,\bpsi;\theta)$.
  26 KB (3,691 words) - 16:20, 25 June 2013
• Testing A joint probability
  ...will be random, so the model becomes a probabilistic one, representing the joint distribution of these random variables. ...of the model will then allow it to be decomposed into submodels, i.e., the joint distribution decomposed into a product of conditional distributions.
  42 KB (6,569 words) - 21:48, 19 April 2013
• The individual approach
  ...be considered in due time, but for now let us concentrate on a continuous data model. A model for continuous data can be represented mathematically as follows:
  35 KB (5,423 words) - 15:58, 28 August 2013
• Model evaluation
  ...evaluating the performance of a model based on the observed data, the same data that was used to build the model. Fair enough, but what then do we mean by ...nate model candidates that do not seem capable of reproducing the observed data.
  36 KB (5,446 words) - 16:22, 25 June 2013

View (previous 20 | next 20) (20 | 50 | 100 | 250 | 500)