We then propose a latent class model for the joint analysis of longitudinal and survival data, along with details on its likelihood and estimation methods. Simulation studies are provided and asymptotic properties of the estimators are investigated. In the end of the thesis, we applied the ...
Joint latent class model is a statistical approach allowing to simultaneously account for two outcomes related to disease progression: A longitudinal measure (for example a biomarker) and time‐to‐event, in the context of a heterogeneous population. Within this approach, the linear mixed model, ...
Zhang, B., Liu, W., Zhang, H., Chen, Q., and Zhang, Z. (2016). Composite likelihood and maximum likelihood methods for joint latent class modeling of disease prevalence and high- dimensional semicontinuous biomarker data. Computational Statistics, 31(2):425-449....
There are two levels of structure in the latent class joint model. First, the uncertainty of latent class membership is specified through a multinomial logistic model. Second, the class-specific marker trajectory and event process are specified parametrically and semiparametrically, under the assumption...
model trained on position (Fig.3i). For comparison, an L1 regression using all neurons achievedR2 = 74% and 16D conv-pi-VAE achievedR2 = 82%. We also tested CEBRA on an additional monkey dataset (mc-maze) presented in the Neural Latent Benchmark37, in which it achieved state...
In underwater acoustic target recognition, there is a lack of massive high-quality labeled samples to train robust deep neural networks, and it is difficult to collect and annotate a large amount of base class data in advance unlike the image recognition
model trained on position (Fig.3i). For comparison, an L1 regression using all neurons achievedR2 = 74% and 16D conv-pi-VAE achievedR2 = 82%. We also tested CEBRA on an additional monkey dataset (mc-maze) presented in the Neural Latent Benchmark37, in which it achieved state...
We introduce a new continuous time model to jointly model stock return and duration between trades. This model include a bivariate Ornstein-Uhlenbeck process for two latent processes: log-volatility of stock returns and log-intensity of the elapsed time between trades. We apply this model to tick...
The joint model is estimated in the maximum likelihood framework. A score test is developed to evaluate the assumption of conditional independence of the longitudinal markers and each cause of progression given the latent classes. In addition, individual dynamic cumulative incidences of each cause of...
The application of Latent Class Clustering (LCC) is a commonly employed technique in crash severity studies to address unobserved heterogeneity [32,33]. However, to the best of our knowledge, the integration of LCC with interpretable ML has not been established and one objective of this study ...