Estimation of graphical models for skew continuous data

2022 ◽  
Author(s):  
Linh H. Nghiem ◽  
Francis K. C. Hui ◽  
Samuel Müller ◽  
A. H. Welsh
Keyword(s):  
Graphical Models ◽  
Continuous Data

Bereavement or Breakup: A Network Approach to Modeling Differential Pathways into Depression

2019 ◽  
Author(s):  
Julian Burger ◽  
Margaret S. Stroebe ◽  
Pasqualina Perrig-Chiello ◽  
Henk A.W. Schut ◽  
Stefanie Spahni ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Graphical Models ◽  
Contextual Factors ◽  
Marital Disruption ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Multiple Time ◽  
Cross Sectional ◽  
Network Analyses ◽  
Within Subjects

Background: Prior network analyses demonstrated that the death of a loved one potentially precedes specific depression symptoms, primarily loneliness, which in turn links to other depressive symptoms. In this study, we extend prior research by comparing depression symptom network structures following two types of marital disruption: bereavement versus separation. Methods: We fitted two Gaussian Graphical Models to cross-sectional data from a Swiss survey of older persons (145 bereaved, 217 separated, and 362 married controls), and compared symptom levels across bereaved and separated individuals. Results: Separated compared to widowed individuals were more likely to perceive an unfriendly environment and oneself as a failure. Both types of marital disruption were linked primarily to loneliness, from where different relations emerged to other depressive symptoms. Amongst others, loneliness had a stronger connection to perceiving oneself as a failure in separated compared to widowed individuals. Conversely, loneliness had a stronger connection to getting going in widowed individuals. Limitations: Analyses are based on cross-sectional between-subjects data, and conclusions regarding dynamic processes on the within-subjects level remain putative. Further, some of the estimated parameters in the network exhibited overlapping confidence intervals and their order needs to be interpreted with care. Replications should thus aim for studies with multiple time points and larger samples. Conclusions: The findings of this study add to a growing body of literature indicating that depressive symptom patterns depend on contextual factors. If replicated on the within-subjects level, such findings have implications for setting up patient-tailored treatment approaches in dependence of contextual factors.

Constructing and model-fitting receiver operator characteristics using continuous data.

2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor
Keyword(s):  
Signal Detection ◽  
Least Squares Method ◽  
Model Fitting ◽  
Simulated Data ◽  
Continuous Variable ◽  
Continuous Data ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Unequal Variance ◽  
Discrete Discrimination

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.

Snapshot Method for Continuous Data Protection Systems

2011 ◽  
Vol 22 (10) ◽  
pp. 2523-2537
Author(s):  
Xiao LI ◽  
Yu-An TAN ◽  
Yuan-Zhang LI
Keyword(s):  
Data Protection ◽  
Continuous Data ◽  
Protection Systems

Graphical Models and Collective Choice

2005 ◽  
Author(s):  
Whitman Richards
Keyword(s):  
Graphical Models ◽  
Collective Choice

TO DETERMINE SKEWNESS, MEAN AND DEVIATION WITH A NEW APPROACH ON CONTINUOUS DATA

2017 ◽  
Vol 73 (2) ◽  
Author(s):  
Mehmet Guven Gunver ◽  
Mustafa Sukru Senocak ◽  
Suphi Vehid
Keyword(s):  
Continuous Data ◽  
New Approach

Design and Evaluation of a Continuous Data Level Auditing System

2014 ◽  
Vol 33 (4) ◽  
pp. 221-245 ◽  
Author(s):  
Alexander Kogan ◽  
Michael G. Alles ◽  
Miklos A. Vasarhelyi ◽  
Jia Wu
Keyword(s):  
Business Processes ◽  
Population Data ◽  
Daily Basis ◽  
Data Availability ◽  
Continuous Data ◽  
Continuous Auditing ◽  
Major Health ◽  
Analytical Monitoring ◽  
Data Level ◽  
Unique Capability

SUMMARY: This study develops a framework for a continuous data level auditing system and uses a large sample of procurement data from a major health care provider to simulate an implementation of this framework. In this framework, the first layer monitors compliance with deterministic business process rules and the second layer consists of analytical monitoring of business processes. A distinction is made between exceptions identified by the first layer and anomalies identified by the second one. The unique capability of continuous auditing to investigate (and possibly remediate) the identified anomalies in “pseudo-real time” (e.g., on a daily basis) is simulated and evaluated. Overall, evidence is provided that continuous auditing of complete population data can lead to superior results, but only when audit practices change to reflect the new reality of data availability. Data Availability: The data are proprietary. Please contact the authors for details.

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