The Experts below are selected from a list of 212589 Experts worldwide ranked by ideXlab platform
Marco Geraci - One of the best experts on this subject based on the ideXlab platform.
-
linear quantile mixed models the lqmm package for laplace quantile regression
Journal of Statistical Software, 2014Co-Authors: Marco GeraciAbstract:Inference in quantile analysis has received considerable attention in the recent years. Linear quantile mixed models (Geraci and Bottai 2014) represent a flexible Statistical Tool to analyze data from sampling designs such as multilevel, spatial, panel or longitudinal, which induce some form of clustering. In this paper, I will show how to estimate conditional quantile functions with random effects using the R package lqmm. Modeling, estimation and inference are discussed in detail using a real data example. A thorough description of the optimization algorithms is also provided.
Yufeng Liu - One of the best experts on this subject based on the ideXlab platform.
-
stepwise multiple quantile regression estimation using non crossing constraints
Statistics and Its Interface, 2009Co-Authors: Yufeng LiuAbstract:Quantile regression is an important Statistical Tool for Statistical modeling. It has been widely used in various fields including econometrics, medicine, and bioinformatics. Despite its popularity in practice, individually estimated quantile regression functions often cross each other and consequently violate the basic properties of quantiles. In this paper we propose a new method for estimating multiple quantile regression functions without crossing. Both linear and kernel quantile regression models are considered. Several numerical examples are presented to illustrate competitive performance of the proposed method.
Michael J Schull - One of the best experts on this subject based on the ideXlab platform.
-
quantile regression a Statistical Tool for out of hospital research
Academic Emergency Medicine, 2003Co-Authors: Peter C Austin, Michael J SchullAbstract:The performance of out-of-hospital systems is frequently evaluated based on the times taken to respond to emergency requests and to transport patients to hospital. The 90th percentile is a common statistic used to measure these indicators, since they reflect performance for most patients. Traditional regression models, which assess how the mean of a distribution varies with changes in patient or system characteristics, are thus of limited use to researchers in out-of-hospital care. In contrast, quantile regression models estimate how specified quantiles (or percentiles) of the distribution of the outcome variable vary with patient or system characteristics. The authors examined the performance of traditional linear regression vs. that of quantile regression to assess the association between hospital transport interval and patient and system characteristics. They demonstrate that richer inferences can be drawn from the data using quantile regression, utilizing data drawn from a study of ambulance diversion and out-of-hospital delay. The results demonstrate that the effect of ambulance diversion upon out-of-hospital transport intervals is not uniform, but is worse on the right tail of the distribution of transport intervals. In other words, ambulance diversion disproportionately affects those patients who already have longer transport intervals. Second, the distribution of transport intervals, conditional on a given set of variables, is positively skewed, and not uniformly or symmetrically distributed. The flexibility of quantile regression models makes them particularly well suited to out-of-hospital research, and they may allow for more relevant evaluation of out-of-hospital system performance.
Hyokyoung G Hong - One of the best experts on this subject based on the ideXlab platform.
-
estimation of time varying reproduction numbers underlying epidemiological processes a new Statistical Tool for the covid 19 pandemic
PLOS ONE, 2020Co-Authors: Hyokyoung G HongAbstract:The coronavirus pandemic has rapidly evolved into an unprecedented crisis. The susceptible-infectious-removed (SIR) model and its variants have been used for modeling the pandemic. However, time-independent parameters in the classical models may not capture the dynamic transmission and removal processes, governed by virus containment strategies taken at various phases of the epidemic. Moreover, few models account for possible inaccuracies of the reported cases. We propose a Poisson model with time-dependent transmission and removal rates to account for possible random errors in reporting and estimate a time-dependent disease reproduction number, which may reflect the effectiveness of virus control strategies. We apply our method to study the pandemic in several severely impacted countries, and analyze and forecast the evolving spread of the coronavirus. We have developed an interactive web application to facilitate readers' use of our method.
Emmanuel I. Unuabonah - One of the best experts on this subject based on the ideXlab platform.
-
small sample corrected akaike information criterion an appropriate Statistical Tool for ranking of adsorption isotherm models
Desalination, 2011Co-Authors: Onoja Akpa, Emmanuel I. UnuabonahAbstract:Abstract Ranking of seven equilibrium isotherm models with various numbers of parameters (Langmuir, Freundlich, Redlich–Peterson, Sip, Langmuir–Freundlich, Fritz–Schlunder-3 parameter and Fritz–Schlunder-4 parameter) for the adsorption of Cu 2+ and Cd 2+ onto Bentonite and modified Bentonite clay was done using the small-sample-corrected Akaike information criterion (AIC c ). It was observed that the Freundlich model ranked first among the adsorption isotherm models considered. In most cases, using the AIC c , the Langmuir model was ranked the second best isotherm model. Modification of Geothite, Humic acid, and Goethite + Humic acid with increase in temperature was observed to affect the Relative Akaike Weight (RAW) which describes the performance of adsorption models relative to the adsorption model with the minimum AIC c estimate. The AIC c was found to rank adsorption isotherm models better than error functions because it is more sensitive to model deviations and takes into consideration the number of parameters in an equilibrium isotherm model. AIC c was found to be more reliable in resolving very close performance of adsorption isotherms. This study showed that the conventional isotherm model, Freundlich, is a better model for describing experimental data from adsorption of Cu 2+ and Cd 2+ onto Bentonite and modified Bentonite adsorbents than models derived from them and having a higher number of parameters.
