scholarly journals A General Conditional-Logistic Model for Affected-Relative-Pair Linkage Studies

1999 ◽  
Vol 65 (6) ◽  
pp. 1760-1769 ◽  
Author(s):  
Jane M. Olson
Keyword(s):  
Logistic Model ◽  
Linkage Studies ◽  
Relative Pair ◽  
Affected Relative ◽  
Affected Relative Pair ◽  
Conditional Logistic Model

On the Power of Affected Relative Pair Designs for Linkage Studies

2004 ◽  
Vol 68 (1) ◽  
pp. 65-68 ◽  
Author(s):  
Z. LI ◽  
J. L. GASTWIRTH
Keyword(s):  
Linkage Studies ◽  
Relative Pair ◽  
Affected Relative ◽  
Affected Relative Pair

scholarly journals A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis

2011 ◽  
Vol 5 (S9) ◽  
Author(s):  
Yeunjoo E Song ◽  
Junghyun Namkung ◽  
Robert W Shields ◽  
Daniel J Baechle ◽  
Sunah Song ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Linkage Analysis ◽  
Nucleotide Polymorphisms ◽  
Relative Pair ◽  
Single Nucleotide ◽  
Linkage Signal ◽  
Affected Relative ◽  
Affected Relative Pair

Prediction of Empirical p Values from Asymptotic p Values for Conditional Logistic Affected Relative Pair Linkage Analysis

2006 ◽  
Vol 61 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Moumita Sinha ◽  
Yeunjoo Song ◽  
Robert C. Elston ◽  
Jane M. Olson ◽  
Katrina A.B. Goddard
Keyword(s):  
Linkage Analysis ◽  
Relative Pair ◽  
P Values ◽  
Affected Relative ◽  
Affected Relative Pair

Candidate Gene Analysis of the Price Foundation Anorexia Nervosa Affected Relative Pair Dataset

2003 ◽  
Vol 2 (1) ◽  
pp. 41-51 ◽  
Author(s):  
A. Bergen ◽  
M. Yeager ◽  
R. Welch ◽  
J. Ganjei ◽  
A. Deep-Soboslay ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Candidate Gene ◽  
Gene Analysis ◽  
Relative Pair ◽  
Candidate Gene Analysis ◽  
Affected Relative ◽  
Affected Relative Pair

Relationship uncertainty linkage statistics (RULS): affected relative pair statistics that model relationship uncertainty

2008 ◽  
Vol 32 (4) ◽  
pp. 313-324 ◽  
Author(s):  
Amrita Ray ◽  
Daniel E. Weeks
Keyword(s):  
Relative Pair ◽  
Affected Relative ◽  
Affected Relative Pair

scholarly journals Transmission Based Conditional Logistic Model for Testing Main and Interaction Effects

2021 ◽  
Vol 11 (05) ◽  
pp. 713-719
Author(s):  
Caixia Li ◽  
Peixing Li
Keyword(s):  
Logistic Model ◽  
Interaction Effects ◽  
Conditional Logistic Model

Real-Time Crash Risk Prediction using Long Short-Term Memory Recurrent Neural Network

2019 ◽  
Vol 2673 (4) ◽  
pp. 314-326 ◽  
Author(s):  
Jinghui Yuan ◽  
Mohamed Abdel-Aty ◽  
Yaobang Gong ◽  
Qing Cai
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Risk Prediction ◽  
Logistic Model ◽  
Short Term Memory ◽  
Crash Risk ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Conditional Logistic Model

With the help of traffic detectors widely deployed along arterial roads and intersections, real-time traffic data are collected and updated in a very short time period, which makes it possible to conduct real-time analysis at signalized intersections. Among them, real-time crash risk prediction is one of the most promising and challenging research topics. This study attempts to predict real-time crash risk by considering time series dependency with the employment of a long short-term memory recurrent neural network (LSTM-RNN) algorithm. Also, the synthetic minority over-sampling technique (SMOTE) was utilized in this study to generate a balanced training dataset for algorithm training. In comparison, a conditional logistic model was developed based on matched case control design. Both models were evaluated based on the real-world unbalanced test dataset rather than an artificially balanced dataset. The comparison results indicate that the LSTM-RNN with SMOTE outperforms the conditional logistic model. The methods and findings of this study attempt to verify the feasibility of real-time crash risk prediction by using LSTM-RNN with over-sampled dataset (SMOTE).

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