Results 341 to 350 of about 10,224,594 (387)

Confidence distributions in statistical inference

AIP Conference Proceedings, 2011
This paper reviews the new methodology for statistical inferences. Point estimators, confidence intervals and p—values are fundamental tools for frequentist statisticians. Confidence distributions, which can be viewed as “distribution estimators”, are often convenient for constructing all of the above statistical procedures and more.
Sergey Bityukov, Nikolai Krasnikov, Saralees Nadarajah, Vera Smirnova, Ali Mohammad-Djafari, Jean-François Bercher, Pierre Bessiére   +6 more
openaire   +2 more sources

Model-averaged confidence distributions

Environmental and Ecological Statistics, 2019
Model averaging is commonly used to allow for model uncertainty in parameter estimation. As well as providing a point estimate that is a natural compromise between the estimates from different models, it also provides confidence intervals with better coverage properties, compared to those based on a single best model.
David Fletcher, Peter W. Dillingham, Peter W. Dillingham, Jiaxu Zeng   +3 more
openaire   +2 more sources

Confidence Distribution for the Ability Parameter of the Rasch Model

Psychometrika, 2021
In this paper, we consider the Rasch model and suggest novel point estimators and confidence intervals for the ability parameter. They are based on a proposed confidence distribution (CD) whose construction has required to overcome some difficulties essentially due to the discrete nature of the model. When the number of items is large, the computations
Piero Veronese, Eugenio Melilli
openaire   +4 more sources

Confidence bands for the laplace distribution

Journal of Statistical Computation and Simulation, 1982
Based on a random sample from the Laplace population with unknown shape and scale parameters, one- and two-sided confidence bands on the entire cumulative distribution function and simultaneous confidence intervals for the interval probabilities under the distribution are constructed using Kolmogorov–Smirnov type statistics. Small sample and asymptotic
R. Srinivasan, Robert M. Wharton
openaire   +2 more sources

Confidence intervals for the parameters of the logistic distribution

Biometrika, 1970
where a and b are location and scale parameters. The range of application of the logistic distribution as a probability model to describe random phenomenon covers such areas as psychosensory response systems, population growth, bioassay, life tests and physiochemical phenomena.
William L. Harkness, Lawrence A. Klimko, Charles E. Antle   +2 more
openaire   +2 more sources

Confidence Regions for Distribution Bounds

IEEE Transactions on Reliability, 1980
This paper considers the problem of constructing an s-confidence region for a pair of parameters which together mark the bounds of a distribution. The problem is solved, and a table provided, for a rectangular distribution; the solution is then generalized.
openaire   +2 more sources

P-MVSNet: Learning Patch-Wise Matching Confidence Aggregation for Multi-View Stereo

IEEE International Conference on Computer Vision, 2019
Learning-based methods are demonstrating their strong competitiveness in estimating depth for multi-view stereo reconstruction in recent years. Among them the approaches that generate cost volumes based on the plane-sweeping algorithm and then use them ...
Keyang Luo, T. Guan, L. Ju, Haipeng Huang, Yawei Luo   +4 more
semanticscholar   +1 more source

Sampling Distributions I: Confidence

1998
You have been studying a strategy for data collection which involves the use of randomization. At first glance, it might seem that this deliberate introduction of uncertainty would only compound the problem of drawing reliable conclusions from the data collected.
Beth Chance, Allan J. Rossman
openaire   +2 more sources

Modified information criterion for regular change point models based on confidence distribution

Environmental and Ecological Statistics, 2021
Suthakaran Ratnasingam, Wei Ning
semanticscholar   +1 more source

Empirical likelihood ratio confidence intervals for a single functional

, 1988
SUMMARY The empirical distribution function based on a sample is well known to be the maximum likelihood estimate of the distribution from which the sample was taken.
A. Owen
semanticscholar   +1 more source