Results 11 to 20 of about 26,568 (218)

Confidence intervals for ratio of means of delta-lognormal distributions based on left-censored data with application to rainfall data in Thailand [PDF]

PeerJ, 2023
Thailand is a country that is prone to both floods and droughts, and these natural disasters have significant impacts on the country’s people, economy, and environment. Estimating rainfall is an important part of flood and drought prevention.
Warisa Thangjai, Sa-Aat Niwitpong
doaj   +2 more sources

Cost-effectiveness of an intervention to reduce emergency re-admissions to hospital among older patients. [PDF]

PLoS ONE, 2009
BACKGROUND: The objective is to estimate the cost-effectiveness of an intervention that reduces hospital re-admission among older people at high risk.
Nicholas Graves, Mary Courtney, Helen Edwards, Anne Chang, Anthony Parker, Kathleen Finlayson   +5 more
doaj   +1 more source

Bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in Thailand [PDF]

PeerJ, 2022
Precipitation and flood forecasting are difficult due to rainfall variability. The mean of a delta-gamma distribution can be used to analyze rainfall data for predicting future rainfall, thereby reducing the risks of future disasters due to excessive or ...
Theerapong Kaewprasert, Sa-Aat Niwitpong, Suparat Niwitpong   +2 more
doaj   +2 more sources

Prokinetics for the treatment of functional dyspepsia: Bayesian network meta-analysis

BMC Gastroenterology, 2017
Background Controversies persist regarding the effect of prokinetics for the treatment of functional dyspepsia (FD). This study aimed to assess the comparative efficacy of prokinetic agents for the treatment of FD.
Young Joo Yang, Chang Seok Bang, Gwang Ho Baik, Tae Young Park, Suk Pyo Shin, Ki Tae Suk, Dong Joon Kim   +6 more
doaj   +1 more source

Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence Intervals

Journal of Probability and Statistics, 2015
Model averaging is a technique used to account for model uncertainty, in both Bayesian and frequentist multimodel inferences. In this paper, we compare the performance of model-averaged Bayesian credible intervals and frequentist confidence intervals ...
Daniel Turek
doaj   +1 more source

Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes

BMC Public Health, 2023
Background The spread of the COVID-19 (SARS-CoV-2) and the surging number of cases across the United States have resulted in full hospitals and exhausted health care workers.
Srikanth Aravamuthan, Juan Francisco Mandujano Reyes, Brian S. Yandell, Dörte Döpfer   +3 more
doaj   +1 more source

Statistical inference for a constant-stress partially accelerated life tests based on progressively hybrid censored samples from inverted Kumaraswamy distribution

PLoS ONE, 2022
In this article, we investigate the problem of point and interval estimations under constant-stress partially accelerated life tests. The lifetime of items under use condition is assumed to follow the two-parameter inverted Kumaraswamy distribution ...
Manal M. Yousef, Salem A. Alyami, Atef F. Hashem   +2 more
doaj   +3 more sources

Interval Estimation for the Two-Parameter Exponential Distribution Based on Upper Record Value Data Using Bayesian Approaches

Mathematics
Using upper record value data, we provide a credible interval estimate for the scale parameter of a two-parameter exponential distribution based on Bayesian methods.
Shu-Fei Wu
doaj   +1 more source

Evaluation of the accuracy of the IDvet serological test for Mycoplasma bovis infection in cattle using latent class analysis of paired serum ELISA and quantitative real-time PCR on tonsillar swabs sampled at slaughter.

PLoS ONE, 2023
Mycoplasma bovis (Mbovis) was first detected in cattle in New Zealand (NZ) in July 2017. To prevent further spread, NZ launched a world-first National Eradication Programme in May 2018.
Nelly Marquetoux, Matthieu Vignes, Amy Burroughs, Emma Sumner, Kate Sawford, Geoff Jones   +5 more
doaj   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source