Parametric regression. Medical search. Frequent questions

Combining non-parametric models with logistic regression: an application to motor vehicle injury data. (edu.au)
Most published studies focus on techniques such as logistic regression to summarise their results simply in the form of odds ratios. (edu.au)
They have not only identified potential areas of risk largely governed by age and number of years driving experience but can also identify outlier groups and can be used as a precursor to a more detailed logistic regression analysis. (edu.au)
special topics such as logistic regression. (uoguelph.ca)
The FP approach is then compared with the existing non-parametric approaches when error assessment data are available on the entire sample of the study subjects (complete error assessment data) considering covariate measurement error in a multiple logistic regression model. (uaeu.ac.ae)
Hossain, S , Hoque, Z & Hasan, AHMS 2016, ' A Better Alternative to Non-parametric Approaches for Adjusting for Covariate Measurement Errors in Logistic Regression ', Communications in Statistics: Simulation and Computation , vol. 45, no. 8, pp. 2659-2677. (uaeu.ac.ae)
Parametric statistical tests, including multiple logistic regression, allowed analyses. (nursingcenter.com)
Logistic regression in Python tutorial for beginners. (udemy.com)
Using logistic regression and non-parametric statistic methods we also evaluated the effect of specific exposure measures on these tests and the predictive value of these tests on reproductive outcomes. (cdc.gov)
Multivariate logistic regression was used to obtain the significant determinants of smoking. (who.int)
We used logistic regression models to calculate crude and adjusted odds ratios (OR) to explore risk factors for critical outcomes (intensive care unit admission or death) among COVID-19 cases. (who.int)

We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. (ac.ir)
We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett's method, and Durbin's method), The non-parametric model is estimated by using kernel smoothing (Nadaraya Watson), K-Nearest Neighbor smoothing and Median smoothing. (ac.ir)
The Flower Pollination algorithms were employed and structured in building the ecological model and estimating the semi-parametric regression function with measurement errors in the explanatory and dependent variables, then compare the models to choose the best model used in the environmental scope measurement errors, where the comparison between the models is done using the mean square error (MSE). (ac.ir)
For any choice of norms, oping so-called semi-parametric approaches, which typically the resulting generalized SPICE method enjoys the same make some model structure assumptions, although restrain benefits as the regular SPICE method, including being hyper- from making strong model order assumptions, other than as- parameter free, although the choice of norm is shown to suming that the solution is sparse. (lu.se)

Analysis of variance (ANOVA), non - parametric ANOVA. (muni.cz)
This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. (ellibs.com)
Frequently, many researchers and graduate students make assertions such as "I would rather use Analysis of Variance (ANOVA) than regression in my study because ANOVA is simpler and it will provide me with all the information I need. (ericae.net)
Although Cohen (1968) and Falzer (1974) acknowledged the importance of the general linear model in the 60's and 70's, the use of ANOVA methods remained popular through the 80's because of their computational simplicity over other methods such as regression. (ericae.net)

In this paper, we introduce a novel parametric quantile regression model for asymmetric response variables, where the response variable follows a power skew-normal distribution. (mdpi.com)
The fundamental objective of our work is to build a model connecting 2D X-ray information to 3D CT information through regression. (hal.science)
We show that for each predictor it is possible to describe 99% of the variance through a simple up to second order parametric model. (hal.science)
Conduct the weighted least squares regression model. (numxl.com)
The procedure/algorithm uses the H*N data points that are closest to the target value, so every value will construct its own regression model with a (possibly) different data set. (numxl.com)
9] M. Li, Y. Ma and R. Li , Semiparametric regression for measurement error model with heteroscedastic error , J. (ac.ir)
Due to the high noise level of pixel-wise kinetic data in dynamic studies, direct model fitting of the kinetic data on a pixel-by-pixel basis requires many iterations and has high variability, making it unsuitable for generating parametric images of the micro-parameters of tracer kinetic models. (johnshopkins.edu)
In this study, we propose and investigate a spatially-coordinated method for image-wise nonlinear regression that allows good quality parametric images to be generated by direct model fitting. (johnshopkins.edu)
Instead of model fitting the kinetics of each pixel independently, the new method performs regression simultaneously in parallel for all pixels in the image with the intermediate results at each iteration step coordinated among neighboring pixels. (johnshopkins.edu)
Huang, SC & Zhou, Y 1998, ' Spatially-coordinated regression for image-wise model fitting to dynamic pet data for generating parametric images1 ', IEEE Transactions on Nuclear Science , vol. 45, no. 3 PART 2, pp. 1194-1199. (johnshopkins.edu)
Sometimes, these multi-parametric problems can be solved by using advanced model order reduction-MOR-techniques. (springeropen.com)
The solution for any choice of the parameters is then inferred from the prediction of the regression model. (springeropen.com)
We employ Quantile Regression Forest (QRF), which proved to be a powerful method to model past SSY in previous studies, at two nested high-alpine gauges in the Ötztal, Austria, i.e. gauge Vent (98.1 km² catchment area, 28 % glacier cover in 2015) and gauge Vernagt (11.4 km² catchment area, 64 % glacier cover). (copernicus.org)
These two features are modelled nonparametrically, whereas autocorrelations are described by a short memory stationary parametric time series model. (researchgate.net)
Reported dengue cases from localities and Shuttle Radar Topography Mission (SRTM) 90 m digital elevation model (DEM) of study areas have been processed for hotspots, regression model and stream density in the localities of high dengue incidence. (springer.com)
This property can be used in a non-parametric regression model. (tudelft.nl)
Fortunately, computers today can compute complex analyses such as regression, and canonical analysis, however the shift from OVA methods to the general linear model has been gradual. (ericae.net)
Several covariates-age, sex, combined HIV exposure category, CD4 cell counts, antiretroviral treatment and AIDS-defining illnesses-were included in the parametric model to predict factors associated with future mortality. (who.int)
of the residuals of the linear multiple regression model. (lu.se)
We used multivariable Cox regression model to describe the association between patient characteristics and the time to treatment intensification. (bvsalud.org)
It was developed using R statistical software, was based on a previously described model that used non-parametric negative binomial regression and was piloted in several countries. (who.int)
The course covers issues such as characterizing duration distributions and common parametric families, observation schemes (censoring and truncation), nonparametric approaches, basic hazard regression (proportional hazards), the Cox PH model and model diagnostics, discrete-time hazard regression, piece-wise constant hazard model, non-proportional hazards models, and unobserved heterogeneity. (lu.se)

12.3 Parametric and semiparametric estimation. (who.int)

10] H. F. F. Mahmoud , Parametric versus Semi and Nonparametric Regression Models , arXiv preprint arXiv:1906.10221, (2019). (ac.ir)
Studies show that use is low among African Americans and nonparametric regression techniques. (cdc.gov)

Topics covered will include the role of statistics in business decisions, organization of data, frequency distributions, probability, normal and sampling distributions, hypothesis tests, linear regression and an introduction to time series, quality control and operations research. (uoguelph.ca)
Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values). (ellibs.com)

7] Huque, M. H. Huque, H. D. Bondell, R. J. Carroll and L. M. Ryan, Spatial regression with covariate measurement error: A semiparametric approach , Biometrics,72( 3 )(2016) 678-686. (ac.ir)

Simulation results reveal that, in comparable situations, the FP approach performs as good as or better than the competing non-parametric approaches in eliminating the bias that arises in the estimated regression parameters due to covariate measurement errors. (uaeu.ac.ae)
Such modeling could range from parametric approaches to non-parametric ones like Artificial Neural Networks (ANN). (lu.se)
However, this robustness comes with the downside that the non-parametric methods have, in general, larger variance in In this work, we extend the popular sparse iterative covariance- the estimates compared to the parametric approaches, which based estimator (SPICE) by generalizing the formulation to typically in turn are less robust [5]. (lu.se)

Quantile Regression as introduced by Koenker and Bassett (1978) seeks to complement classical linear regression analysis. (wikibooks.org)
Quantile Regression goes beyond this and enables one to pose such a question at any quantile of the conditional distribution function. (wikibooks.org)
The following seeks to introduce the reader to the ideas behind Quantile Regression. (wikibooks.org)
Finally, Quantile Regression is presented, along with an example utilizing the Boston Housing data set. (wikibooks.org)
The objective of this work is to propose parametric quantile regressions based on two asymmetric income distributions: Dagum and Singh-Maddala. (pucv.cl)
The proposed quantile regression models are based on reparameterizations of the original distributions by inserting a quantile parameter. (pucv.cl)
We present the reparameterizations, properties of the distributions, and the quantile regression models with their inferential aspects. (pucv.cl)
We apply the proposed quantile regression models to a household income data set provided by the National Institute of Statistics of Chile. (pucv.cl)
Thus, we conclude that the obtained results favor the Singh-Maddala and Dagum quantile regression models for positive asymmetrically distributed data related to incomes. (pucv.cl)

This week's article comes from the International Statistical Review and looks at sparser ordinal regression models. (statisticsviews.com)
Traditional regressions based on normal distributions are statistical models widely applied. (pucv.cl)
The statistical analysis using multiple linear regression showed that hardiness is a personality trait that explains burnout, presenting different predictive models for each sample. (bvsalud.org)

2008). Bayesian two-stage regression with parametric heteroskedasticity. (helsinki.fi)

As a first step we propose an univariate non-parametric regression on individual predictor variables to explore the non-linearity of the data. (hal.science)
To later combine these univariate models we then replace them with parametric models. (hal.science)

function implements a weighted least squares regression. (numxl.com)

A linear regression identified regional activations to p(Stop), each in correlation with SSRT and motor urgency. (jneurosci.org)

To date, computer-intensive non-parametric modelling procedures such as classification and regression trees (CART) and multivariate adaptive regression splines (MARS) have rarely been used in the analysis of epidemiological studies. (edu.au)

The iterative convex minorant(ICM) algorithm can be used to compute an estimate of a convex regression. (tudelft.nl)

In a nutshell, for a given bandwidth, we calculate the regression value for each data point (using leave-one out cross-validation) and compute the RMSE between regression values and the actual values. (numxl.com)

So, if we have a spike in our data, some of the points in the spike may not be in the regression dataset. (numxl.com)
On the other hand, if Spike is a reference to a remote data point along the x-domain, then those remote data points will be weighted down significantly in the regression. (numxl.com)
Regressions created from experimental or simulated data enable the construction of metamodels, widely used in a variety of engineering applications. (springeropen.com)
In this article, we propose a flexible parametric (FP) approach for adjusting for covariate measurement errors in regression that can accommodate replicated measurements on the surrogate (mismeasured) version of the unobserved true covariate on all the study subjects or on a sub-sample of the study subjects as error assessment data. (uaeu.ac.ae)
he stated that within regression analyses "lie possibilities for more relevant and therefore more powerful exploitation of research data" (p. 426). (ericae.net)
Four different parametric models-exponential, Weibull, log-normal and log-logistic-were applied to data both on HIV-positive cases and on cases diagnosed with AIDS collected through the national HIV/AIDS surveillance system. (who.int)
The collected data were analyzed using descriptive statistics, the non-parametric Friedman test, and categorical regression. (mdpi.com)

Corelation, linear regression. (muni.cz)
This test is based on non-parametric linear regression logic. (magiran.com)
Existing methods for doing this are based either on linear regression, which limits the analysis to linear dependencies, or on trial-and-error procedures. (lu.se)
First, we estimated variable that indicates whether women in our sample had nonparametric local linear regressions (14), which al owed a mammogram during the years 2003 or 2004. (cdc.gov)

We develop refined inference for spatial regression models with predetermined regressors. (researchgate.net)

Many engineering problems involve multi-parametric physics whose corresponding multi-parametric solutions can be viewed as a sort of computational vademecum that, once computed offline, can be then used in a variety of real-time engineering applications including optimization, inverse analysis, uncertainty propagation or simulation based control. (springeropen.com)
I have read the manuscript entitled "Projecting sediment export from two highly glacierized alpine catchments under climate change: Exploring non-parametric regression as an analysis tool" submitted to HESS for possible publishing. (copernicus.org)
Hotspot analysis highlights the areas of high dengue incidence while regression analysis shows the relationship between the population and the areas of localities with the dengue incidence. (springer.com)
In regression analysis, this equation is called a regression equation. (ericae.net)
Flexible parametric survival analysis methods were used to analyse time to mortality and also time to revision. (bmj.com)
Categorical regression analysis indicated that the other family members affected students' recycling behavior to a high degree, whereas students' parents' occupation and students' participation in environmental education programs at school exerted a lower influence on their recycling behavior. (mdpi.com)

which are used for Local Regression and Kernel Regression respectively. (numxl.com)
Please click the button below to download the Local & Kernel Regression (Non-Parametric) example. (numxl.com)

Parametric and non - parametric methods. (muni.cz)
4) parametric methods. (lu.se)

Furthermore, the paper illustrates how each of these analyses produce a synthetic variable, like the Yhat variable in regression. (ericae.net)

We utilize the general framework of the FP approach proposed by Hossain and Gustafson in 2009 for adjusting for covariate measurement errors in regression. (uaeu.ac.ae)

Parametric survival models have not previously been applied to survival following a diagnosis of HIV/AIDS in Australia. (who.int)

function implements a localized regression algorithm - LOESS (or LOWESS) , aka the Savitzky-Golay filter . (numxl.com)

Update to version 3.0.17.3: + This is a fixup release which fixes a regression that could cause a lack of audio for adaptive streaming playback. (rpmfind.net)

However flexible, non-parametric techniques such as CART and MARS can provide more informative and attractive models whose individual components can be displayed graphically. (edu.au)

The sample contained variables on breast cancer screen- ing, age, insurance plan type, morbidity level, income, and Regression techniques race. (cdc.gov)

Calculate the regression value at value X. (numxl.com)

In that case, one possibility consists in solving the problem for a sample of the parametric values and creating a regression from all the computed solutions. (springeropen.com)

Non-parametric test. (lu.se)

They proceed by constructing the parametric solution of the parametric problem from a number of high-fidelity solutions performed offline. (springeropen.com)
SPICE and a penalized regression problem, both for the case present in the signal. (lu.se)

Among these techniques we can mention standard polynomial approximations on sparsely sampled parametric domains. (springeropen.com)
Our outcome of interest was a We used two regression techniques. (cdc.gov)

Anatomy1

Organisms2

Diseases35

Chemicals and Drugs9

Analytical, Diagnostic and Therapeutic Techniques and Equipment85

Psychiatry and Psychology25

Phenomena and Processes44

Disciplines and Occupations2

Anthropology, Education, Sociology and Social Phenomena20

Technology, Industry, Agriculture1

Humanities1

Information Science15

Named Groups10

Health Care106

Geographicals30

Logistic regression11

Semi-parametric4

ANOVA4

Model22

Estimation1

Nonparametric regression2

Hypothesis tests2

Covariate measurement error1

Approaches3

Quantile9

Statistical3

Bayesian1

Univariate2

Weighted least s1

Correlation1

Multivariate1

Iterative1

Compute1

Data7

Linear4

Spatial1

Analysis6

Kernel2

Methods2

Analyses1

Measurement errors1

Survival1

Algorithm1

Adaptive1

Flexible1

Variables1

Calculate1

Sample1

Test1

Problem2

Techniques2