A Latent Process Model for Temporal Extremes

2013 ◽  
Vol 41 (3) ◽  
pp. 606-621 ◽  
Author(s):  
Paola Bortot ◽  
Carlo Gaetan
Keyword(s):  
Process Model ◽  
Latent Process

scholarly journals Attribute Fusion in a Latent Process Model for Time Series of Graphs

2013 ◽  
Vol 61 (7) ◽  
pp. 1721-1732 ◽  
Author(s):  
Minh Tang ◽  
Youngser Park ◽  
Nam H. Lee ◽  
Carey E. Priebe
Keyword(s):  
Time Series ◽  
Process Model ◽  
Latent Process

A latent process model approach to improve the utility of indicator species

Oikos ◽  
2020 ◽  
Vol 129 (12) ◽  
pp. 1753-1762
Author(s):  
Jill Fleming ◽  
Chris Sutherland ◽  
Sean C. Sterrett ◽  
Evan H. Campbell Grant
Keyword(s):  
Indicator Species ◽  
Process Model ◽  
Latent Process ◽  
Model Approach

scholarly journals Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study

Parasitology ◽  
2016 ◽  
Vol 143 (7) ◽  
pp. 821-834 ◽  
Author(s):  
MAFALDA VIANA ◽  
GABRIEL M. SHIRIMA ◽  
KUNDA S. JOHN ◽  
JULIE FITZPATRICK ◽  
RUDOVICK R. KAZWALA ◽  
...  
Keyword(s):  
Process Model ◽  
Epidemiological Data ◽  
Human Infection ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Northern Tanzania ◽  
Animal Infection ◽  
Latent Process ◽  
Level Of Understanding

SUMMARYEpidemiological data are often fragmented, partial, and/or ambiguous and unable to yield the desired level of understanding of infectious disease dynamics to adequately inform control measures. Here, we show how the information contained in widely available serology data can be enhanced by integration with less common type-specific data, to improve the understanding of the transmission dynamics of complex multi-species pathogens and host communities. Using brucellosis in northern Tanzania as a case study, we developed a latent process model based on serology data obtained from the field, to reconstruct Brucella transmission dynamics. We were able to identify sheep and goats as a more likely source of human and animal infection than cattle; however, the highly cross-reactive nature of Brucella spp. meant that it was not possible to determine which Brucella species (B. abortus or B. melitensis) is responsible for human infection. We extended our model to integrate simulated serology and typing data, and show that although serology alone can identify the host source of human infection under certain restrictive conditions, the integration of even small amounts (5%) of typing data can improve understanding of complex epidemiological dynamics. We show that data integration will often be essential when more than one pathogen is present and when the distinction between exposed and infectious individuals is not clear from serology data. With increasing epidemiological complexity, serology data become less informative. However, we show how this weakness can be mitigated by integrating such data with typing data, thereby enhancing the inference from these data and improving understanding of the underlying dynamics.

A latent process model for the pricing of corporate securities

2008 ◽  
Vol 69 (3) ◽  
pp. 439-455 ◽  
Author(s):  
Masaaki Kijima ◽  
Teruyoshi Suzuki ◽  
Keiichi Tanaka
Keyword(s):  
Process Model ◽  
Corporate Securities ◽  
Latent Process

Condition Monitoring Using a Latent Process Model with an Application to Sheet Metal Stamping Processes

2005 ◽  
Vol 127 (2) ◽  
pp. 376-385 ◽  
Author(s):  
Xiaoli Li ◽  
R. Du
Keyword(s):  
Sheet Metal ◽  
Condition Monitoring ◽  
Process Model ◽  
Generative Topographic Mapping ◽  
Monitoring Method ◽  
Practical Applications ◽  
Time Frequency ◽  
Sheet Metal Stamping ◽  
Metal Stamping ◽  
Latent Process

This paper presents a new condition monitoring method based on a latent process model. The method consists of three steps. First, a sensor signal is modeled by a latent process model that is a combination of a time-varying auto-regression model and a dynamic linear model, which decomposes the signal into several components, and each component represents a different part of the monitored system with different time-frequency behavior. Based on the latent process model, important features are extracted. Finally, using the generative topographic mapping, the selected features are mapped to a lower (two)-dimension space for classification. The proposed method is tested in condition monitoring of sheet metal stamping processes. A large number of experiments were conducted. In particular, two cases are presented in detail. From the testing results, it is found that the proposed method is able to detect various defects with a success rate around 98%. This result is significantly better than the conventional artificial neural network method. In addition, the new method is a self-organizing method and hence, little training is necessary. These advantages make the method very attractive for practical applications.

Semi-parametric latent process model for longitudinal ordinal data: Application to cognitive decline

2010 ◽  
Vol 29 (26) ◽  
pp. 2723-2731 ◽  
Author(s):  
Hélène Jacqmin-Gadda ◽  
Cécile Proust-Lima ◽  
Hélène Amiéva
Keyword(s):  
Cognitive Decline ◽  
Ordinal Data ◽  
Process Model ◽  
Data Application ◽  
Latent Process

A latent process model for forecasting multiple time series in environmental public health surveillance

2016 ◽  
Vol 35 (18) ◽  
pp. 3085-3100 ◽  
Author(s):  
Kathryn T. Morrison ◽  
Gavin Shaddick ◽  
Sarah B. Henderson ◽  
David L. Buckeridge
Keyword(s):  
Public Health ◽  
Time Series ◽  
Process Model ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Environmental Public Health ◽  
Latent Process
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