Estimating Adaptive Individual Interests and Needs Based on Online Local Variational Inference for a Logistic Regression Mixture Model

2018 ◽  
Author(s):  
Ryosuke Konishi ◽  
Fumito Nakamura ◽  
Yasushi Kiyoki
Keyword(s):  
Logistic Regression ◽  
Mixture Model ◽  
Variational Inference ◽  
Regression Mixture

scholarly journals Incorporation of Heterogeneity in a Case-Control Study Through a Mixture Model

2020 ◽  
Author(s):  
Subrata Paul ◽  
Stephanie A. Santorico
Keyword(s):  
Logistic Regression ◽  
Mixture Model ◽  
Genetic Variants ◽  
Complex Traits ◽  
Genetic Variant ◽  
Case Control Study ◽  
Case Control ◽  
Human Diseases ◽  
Disease Outcome ◽  
Control Study

AbstractMost common human diseases and complex traits are etiologically heterogeneous. Genome-wide Association Studies (GWAS) aim to discover common genetic variants that are associated with complex traits, typically without considering heterogeneity. Heterogeneity, as well as im-precise phenotyping, significantly reduces the power to find genetic variants associated with human diseases and complex traits. Disease subtyping through unsupervised clustering techniques such as latent class analysis can explain some of the heterogeneity; however, subtyping methods do not typically incorporate heterogeneity into the association framework. Here, we use a finite mixture model with logistic regression to incorporate heterogeneity into the association testing framework for a case-control study. In the proposed method, the disease outcome is modeled as a mixture of two binomial distributions. One of the component distributions refers to the subgroup of the population for which the genetic variant is not associated with the disease outcome and another component distribution corresponds to the subgroup for which the genetic variant is associated with the disease outcome. The mixing parameter corresponds to the proportion of the population for which the genetic variant is associated with the disease outcome. A simulation study of a trait with differing levels of prevalence, SNP minor allele frequency, and odds ratio was performed, and effect size estimates compared between the models with and without incorporating heterogeneity. The proposed mixture model yields lower bias of odds ratios while having comparable power compared to classical logistic regression.

Predictive Distribution of the Dirichlet Mixture Model by Local Variational Inference

2013 ◽  
Vol 74 (3) ◽  
pp. 359-374 ◽  
Author(s):  
Zhanyu Ma ◽  
Arne Leijon ◽  
Zheng-Hua Tan ◽  
Sheng Gao
Keyword(s):  
Mixture Model ◽  
Predictive Distribution ◽  
Variational Inference ◽  
Dirichlet Mixture

Nonlinear logistic regression mixture experiment modeling for binary data using dimensionally reduced components

2019 ◽  
Vol 36 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Bryan A. Stanfill ◽  
Greg F. Piepel ◽  
John D. Vienna ◽  
Scott K. Cooley
Keyword(s):  
Logistic Regression ◽  
Binary Data ◽  
Mixture Experiment ◽  
Regression Mixture

scholarly journals Regression Mixture Model Clustering of Multimodel Ensemble Forecasts of Hurricane Sandy: Partition Characteristics

2016 ◽  
Vol 144 (10) ◽  
pp. 3825-3846 ◽  
Author(s):  
Alex M. Kowaleski ◽  
Jenni L. Evans
Keyword(s):  
Mixture Model ◽  
Forecast Error ◽  
Potential Temperature ◽  
Lower Troposphere ◽  
Hurricane Sandy ◽  
Rand Index ◽  
Ensemble Prediction ◽  
Adjusted Rand Index ◽  
Cluster Assignment ◽  
Regression Mixture

Track and cyclone phase space (CPS) forecasts of Hurricane Sandy from four global ensemble prediction systems are clustered using regression mixture models. Bayesian information criterion, cluster assignment strength, and mean-squared forecast error are used to select optimal model specifications. Fourth-order (third order) polynomials for 168-h forecasts (60-h forecast segments) and 5 (6) clusters for track (CPS) forecasts are selected. Mean cluster paths from eight initialization times show that track and CPS clustering meaningfully partition potential tracks and structural evolutions, distilling a large number of ensemble members into several representative and distinct solutions. Rand index and adjusted Rand index calculations demonstrate a relationship between track and CPS cluster membership for both 168-h forecasts and 60-h forecast segments, indicating that certain tracks are preferentially associated with certain structural evolutions. These relationships are explained in greater detail using forecasts initialized at 0000 UTC 25 October. Storm-centered cluster composite maps of 500-hPa geopotential height and 850-hPa equivalent potential temperature for the 120-h forecast valid at 0000 UTC 30 October (initialized at 0000 UTC 25 October) indicate that both track and CPS clustering successfully capture variations in the Sandy–trough interaction and the strength of the lower-troposphere warm core of Sandy at the time of observed landfall. Together, these results illustrate the relationship between the track and structural evolution of Sandy and suggest the potential of multiensemble mixture-model path clustering for tropical cyclone forecasting.

scholarly journals Cluster Analysis of Typhoon Tracks. Part I: General Properties

2007 ◽  
Vol 20 (14) ◽  
pp. 3635-3653 ◽  
Author(s):  
Suzana J. Camargo ◽  
Andrew W. Robertson ◽  
Scott J. Gaffney ◽  
Padhraic Smyth ◽  
Michael Ghil
Keyword(s):  
Tropical Cyclone ◽  
Mixture Model ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Southern Oscillation ◽  
Companion Paper ◽  
Probabilistic Clustering ◽  
East And Southeast Asia ◽  
Regression Mixture ◽  
Typhoon Tracks

Abstract A new probabilistic clustering technique, based on a regression mixture model, is used to describe tropical cyclone trajectories in the western North Pacific. Each component of the mixture model consists of a quadratic regression curve of cyclone position against time. The best-track 1950–2002 dataset is described by seven distinct clusters. These clusters are then analyzed in terms of genesis location, trajectory, landfall, intensity, and seasonality. Both genesis location and trajectory play important roles in defining the clusters. Several distinct types of straight-moving, as well as recurving, trajectories are identified, thus enriching this main distinction found in previous studies. Intensity and seasonality of cyclones, though not used by the clustering algorithm, are both highly stratified from cluster to cluster. Three straight-moving trajectory types have very small within-cluster spread, while the recurving types are more diffuse. Tropical cyclone landfalls over East and Southeast Asia are found to be strongly cluster dependent, both in terms of frequency and region of impact. The relationships of each cluster type with the large-scale circulation, sea surface temperatures, and the phase of the El Niño–Southern Oscillation are studied in a companion paper.

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