NDLI: Comparing mixture estimates by parametric bootstrapping likelihood ratios

Please wait, while we are loading the content...

The effect on model identifiability of allowing different relocation rates for live and dead animals in the combined analysis of telemetry and recapture data

Bootstrap Confidence intervals for the Shannon biodiversity index: A simulation study

Comparing mixture estimates by parametric bootstrapping likelihood ratios

Confidence interval procedures for the probability of disease transmission in Multiple-Vector-Transfer designs

Comparison of spatial variables over subregions using a block bootstrap

Large wind speeds: Modeling and outlier detection

Journal of Agricultural, Biological, and Environmental Statistics : Volume 8

Journal of Agricultural, Biological, and Environmental Statistics : Volume 7

Journal of Agricultural, Biological, and Environmental Statistics : Volume 6

Comparing mixture estimates by parametric bootstrapping likelihood ratios

Content Provider	Springer Nature Link
Author	Reynolds, Joel H. Templin, William D.
Copyright Year	2004
Abstract	Wildlife managers and researchers often need to estimate the relative contributions of distinct populations in a miture of organisms. Increasingly, there is interest in comparing these mixture contributions across space or time. Comparisons usually just check for overlap in the interval estimates for each population contribution from each mixture. This method inflates Type I error rates, has limited power due to its focus on marginal comparisons, and employs a fundamentally inappropriate measure of mixture difference. Given the difficulty of defining an appropriate measure of mixture difference, a powerful alternative is to compare mixtures using a likelihood ratio test. In applications where the standard asymptotic theory does not hold, the null reference distribution can be obtained through parametric bootstrapping. In addition to testing simple hypotheses, a likelihood ratio framework encourages modeling the change in mixture contributions as a function of covariates. The method is demonstrated with an analysis of potential sampling bias in the estimation of population contributions to the commercial sockeye, salmon (Oncorhynchus nerka) fishery in Upper Cook Inlet, Alaska.
Starting Page	57
Ending Page	74
Page Count	18
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	9
Issue Number	1
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2004-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Compositional data Compositional difference discrete mixture analysis Genetic stock identification Mixed stock analysis Mixture homogeneity Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Managing the Essential Zeros in Quantitative Fatty Acid Signature Analysis

On the Residual Plot in a Mixture Model

Model-Based clustering for cross-sectional time series data

Incorporating variance uncertainty into a power analysis of monitoring designs

Randomly Truncated Nonlinear Mixed-Effects Models

A Likelihood-Based Model of Fish Growth With Multiple Length Frequency Data

A Sequential Monte Carlo Approach for MLE in a Plant Growth Model

A Baseline Category Logit Model for Assessing Competing Strains of Rhizobium Bacteria

On the Compositional Analysis of Fatty Acids in Pork

Comparing mixture estimates by parametric bootstrapping likelihood ratios

Similar Documents

Managing the Essential Zeros in Quantitative Fatty Acid Signature Analysis

On the Residual Plot in a Mixture Model

Model-Based clustering for cross-sectional time series data

Incorporating variance uncertainty into a power analysis of monitoring designs

Randomly Truncated Nonlinear Mixed-Effects Models

A Likelihood-Based Model of Fish Growth With Multiple Length Frequency Data

A Sequential Monte Carlo Approach for MLE in a Plant Growth Model

A Baseline Category Logit Model for Assessing Competing Strains of Rhizobium Bacteria

On the Compositional Analysis of Fatty Acids in Pork

Comparing mixture estimates by parametric bootstrapping likelihood ratios