Nonparametric estimation of the log odds ratio for sparse data by kernel smoothing

2011 ◽  
Vol 81 (12) ◽  
pp. 1802-1807 ◽  
Author(s):  
Ziqi Chen ◽  
Ning-Zhong Shi ◽  
Wei Gao
Keyword(s):  
Nonparametric Estimation ◽  
Odds Ratio ◽  
Kernel Smoothing ◽  
Sparse Data ◽  
Log Odds

scholarly journals On sufficient variable screening using log odds ratio filter

2022 ◽  
Vol 16 (1) ◽  
Author(s):  
Baoying Yang ◽  
Wenbo Wu ◽  
Xiangrong Yin
Keyword(s):  
Odds Ratio ◽  
Variable Screening ◽  
Log Odds

What do (some of) our association measures measure (most)? Association?

2021 ◽  
Author(s):  
Stefan Th. Gries
Keyword(s):  
Corpus Linguistics ◽  
Odds Ratio ◽  
Association Measure ◽  
Measures Of Association ◽  
Association Measures ◽  
Log Odds ◽  
Dispersion Measures ◽  
Corpus Data ◽  
Behavior Supports ◽  
True Association

Abstract This paper discusses the degree to which some of the most widely-used measures of association in corpus linguistics are not particularly valid in the sense of actually measuring association rather than some amalgam of a lot of frequency and a little association. The paper demonstrates these issues on the basis of hypothetical and actual corpus data and outlines implications of the findings. I then outline how to design an association measure that only measures association and show that its behavior supports the use of the log odds ratio as a true association-only measure but separately from frequency; in addition, this paper sets the stage for an analogous review of dispersion measures in corpus linguistics.

Methods of Estimation in Log Odds Ratio Regression Models

Biometrics ◽  
1986 ◽  
Vol 42 (4) ◽  
pp. 949 ◽  
Author(s):  
N. E. Breslow ◽  
J. Cologne
Keyword(s):  
Odds Ratio ◽  
Regression Models ◽  
Log Odds

NONPARAMETRIC ESTIMATION OF VOLATILITY FUNCTIONS: THE LOCAL EXPONENTIAL ESTIMATOR

2002 ◽  
Vol 18 (4) ◽  
pp. 985-991 ◽  
Author(s):  
Flavio A. Ziegelmann
Keyword(s):  
Nonparametric Estimation ◽  
Simulation Study ◽  
Kernel Smoothing ◽  
Asymptotic Properties ◽  
Superior Performance ◽  
Linear Estimator ◽  
Smoothing Techniques ◽  
Conditional Mean ◽  
Fully Adaptive ◽  
Local Linear Estimator

Kernel smoothing techniques free the traditional parametric estimators of volatility from the constraints related to their specific models. In this paper the nonparametric local exponential estimator is applied to estimate conditional volatility functions, ensuring its nonnegativity. Its asymptotic properties are established and compared with those for the local linear estimator. It theoretically enables us to determine when the exponential is expected to be superior to the linear estimator. A very strong and novel result is achieved: the exponential estimator is asymptotically fully adaptive to unknown conditional mean functions. Also, our simulation study shows superior performance of the exponential estimator.

Residual Plots for Log Odds Ratio Regression Models

Biometrics ◽  
1991 ◽  
Vol 47 (3) ◽  
pp. 1135 ◽  
Author(s):  
Masaaki Tsujitani ◽  
Gary G. Koch
Keyword(s):  
Odds Ratio ◽  
Regression Models ◽  
Log Odds ◽  
Residual Plots

scholarly journals Bipolar disorder and cannabis use: a bidirectional two-sample Mendelian randomization study

2020 ◽  
Author(s):  
Oskar Hougaard Jefsen ◽  
Maria Speed ◽  
Doug Speed ◽  
Søren Dinesen Østergaard
Keyword(s):  
Bipolar Disorder ◽  
Standard Deviation ◽  
Odds Ratio ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Cannabis Use ◽  
Genome Wide ◽  
Log Odds ◽  
The Relationship ◽  
Mr Study

AbstractAimsCannabis use is associated with a number of psychiatric disorders, however the causal nature of these associations has been difficult to establish. Mendelian randomization (MR) offers a way to infer causality between exposures with known genetic predictors (genome-wide significant single nucleotide polymorphisms (SNPs)) and outcomes of interest. MR has previously been applied to investigate the relationship between lifetime cannabis use (having ever used cannabis) and schizophrenia, depression, and attention-deficit / hyperactivity disorder (ADHD), but not bipolar disorder, representing a gap in the literature.MethodsWe conducted a two-sample bidirectional MR study on the relationship between bipolar disorder and lifetime cannabis use. Genetic instruments (SNPs) were obtained from the summary statistics of recent large genome-wide association studies (GWAS). We conducted a two-sample bidirectional MR study on the relationship between bipolar disorder and lifetime cannabis use, using inverse-variance weighted regression, weighted median regression and Egger regression.ResultsGenetic liability to bipolar disorder was significantly associated with an increased risk of lifetime cannabis use: scaled log-odds ratio (standard deviation) = 0.0174 (0.039); P-value = 0.00001. Genetic liability to lifetime cannabis use showed no association with the risk of bipolar disorder: scaled log-odds ratio (standard deviation) = 0.168 (0.180); P-value = 0.351. The sensitivity analyses showed no evidence for pleiotropic effects.ConclusionsThe present study finds evidence for a causal effect of liability to bipolar disorder on the risk of using cannabis at least once. No evidence was found for a causal effect of liability to cannabis use on the risk of bipolar disorder. These findings add important new knowledge to the understanding of the complex relationship between cannabis use and psychiatric disorders.

Sign in / Sign up

Export Citation Format

Share Document