scholarly journals Estimation of time-varying decision thresholds from the choice and reaction times with no assumption on the shape

2016 ◽  
Author(s):  
Yul Hyoung Ryul Kang

AbstractWhen a decision is made based on a series of samples of evidence, the threshold for decision may change over time. Here we propose a fast heuristic algorithm that estimates the time-varying thresholds without making an assumption on their shape. The algorithm gives an approximate best estimate of the time-varying thresholds for all time considered when all other parameters are fixed. The algorithm outperforms conventional methods that gradually adjust the threshold estimates during fitting, when fitting time is limited.

Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos ◽  
Annette Skowronska

Many repairable systems degrade with time and are subjected to time-varying loads. Their characteristics may change over time considerably, making the assessment of their performance and hence their design difficult. To address this issue, we introduce in this paper the concept of flexible design of repairable systems under time-dependent reliability considerations. In flexible design, the system can be modified in the future to accommodate uncertain events. As a result, regardless of how uncertainty resolves itself, a modification is available that will keep the system close to optimal provided failure events have been properly characterized. We discuss how flexible design of repairable systems requires a fundamentally new approach and demonstrate its advantages using the design of a hydrokinetic turbine. Our results show that long-term metrics are improved when time-dependent characteristics and flexibility are considered together.


Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 2005
Author(s):  
Jilber Urbina ◽  
Miguel Santolino ◽  
Montserrat Guillen

The covariance allocation principle is one of the most widely used capital allocation principles in practice. Risks change over time, so capital risk allocations should be time-dependent. In this paper, we propose a dynamic covariance capital allocation principle based on the variance-covariance of risks that change over time. The conditional correlation of risks is modeled by means of a dynamic conditional correlation (DCC) model. Unlike the static approach, we show that in our dynamic capital allocation setting, the distribution of risk capital allocations can be estimated, and the expected future allocations of capital can be predicted, providing a deeper understanding of the stochastic multivariate behavior of risks. The methodology presented in the paper is illustrated with an example involving the investment risk in a stock portfolio.


2020 ◽  
Author(s):  
Casper J Albers ◽  
Laura Francina Bringmann

Recent studies have shown that emotion dynamics such as inertia (i.e., autocorrelation) can change over time. Importantly, current methods can only detect either gradual or abrupt changes in inertia. This means that researchers have to choose a priori whether they expect the change in inertia to be gradual or abrupt. This will leave researchers in the dark regarding when and how the change in inertia occurred. Therefore in this article we use a new model: the time-varying change point autoregressive (TVCP-AR) model. The TVCP-AR model can detect both gradual and abrupt changes in emotion dynamics. More specifically, we show that the inertia of positive affect and negative affect measured in one individual differ qualitativelyin how they change over time. Whereas the inertia of positive affect increased only gradually over time, negative affect changed both in a gradual and abrupt fashion over time. This illustrates the necessity of being able to model both gradual and abrupt changes in order to detect meaningful quantitative and qualitative differences in temporal emotion dynamics.


2009 ◽  
Author(s):  
Brian Garbarini ◽  
Hung-Bin Sheu ◽  
Dana Weber

2010 ◽  
Author(s):  
Sam Nordberg ◽  
Louis G. Castonguay ◽  
Benjamin Locke

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