Prevalence proportion ratios: estimation and hypothesis testing

Prevalence proportion ratios: estimation and hypothesis testing.

BACKGROUND Recent communications have argued that often it may not be appropriate to analyse cross-sectional studies of prevalent outcomes with logistic regression models. The purpose of this communication is to compare three methods that have been proposed for application to cross sectional studies: (1) a multiplicative generalized linear model, which we will call the log-binomial model, (2) a...

Hypothesis Testing and Interval Estimation

1 Topics for this Module 1. An Idealistic Special Case — When is Known. 2. Con…dence Interval Estimation (a) Taking a Stroll with Mr. Mu 3. Hypothesis Testing (a) Parameter Spaces and Sample Spaces (b) Partitioning the Parameter Space (c) Partitioning the sample space (d) Raw Score Rejection Rules (e) Error Rates i. Type I Error ii. Type II Error (f) Statistical Power i. Power vs. Precision 4. ...

Surface classification: hypothesis testing and parameter estimation

A 3D surface classification method based upon the quadric surface model is described. This technique does not require the points from the surface to lie on a grid. A sample of surface points is classified as planar or nonplanar through two hypothesis testa If the sample is nonplanar, curvature features are evaluated at each point to classify the sample as spherical, cylindrical, or conical. A n...

Integrated Objective Bayesian Estimation and Hypothesis Testing

The complete final product of Bayesian inference is the posterior distribution of the quantity of interest. Important inference summaries include point estimation, region estimation, and precise hypotheses testing. Those summaries may appropriately be described as the solution to specific decision problems which depend on the loss function chosen. The use of a continuous loss function leads to ...

Large Sample Estimation and Hypothesis Testing*