scholarly journals Interval estimation of the mean response in a log-regression model

2006 ◽  
Vol 25 (12) ◽  
pp. 2125-2135 ◽  
Author(s):  
Jianrong Wu ◽  
A. C. M. Wong ◽  
Wei Wei
Keyword(s):  
Regression Model ◽  
Interval Estimation ◽  
The Mean

PARAMETER ESTIMATION ON HURDLE POISSON REGRESSION MODEL WITH CENSORED DATA

2012 ◽  
Vol 57 (1) ◽  
Author(s):  
SEYED EHSAN SAFFAR ◽  
ROBIAH ADNAN ◽  
WILLIAM GREENE
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Goodness Of Fit ◽  
Poisson Model ◽  
Likelihood Method ◽  
Poisson Regression Model ◽  
Response Variable ◽  
The Mean ◽  
Over Dispersion

A Poisson model typically is assumed for count data. In many cases, there are many zeros in the dependent variable and because of these many zeros, the mean and the variance values of the dependent variable are not the same as before. In fact, the variance value of the dependent variable will be much more than the mean value of the dependent variable and this is called over–dispersion. Therefore, Poisson model is not suitable anymore for this kind of data because of too many zeros. Thus, it is suggested to use a hurdle Poisson regression model to overcome over–dispersion problem. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle Poisson regression model is introduced on count data with many zeros. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness–of–fit for the regression model is examined. We study the effects of right censoring on estimated parameters and their standard errors via an example.

High Correlation between Ethanol Concentrations in Postmortem Femoral Blood and in Alternative Biological Specimens, but Large Uncertainty When the Linear Regression Model Was Used for Prediction in Individual Cases

2020 ◽  
Vol 44 (5) ◽  
pp. 415-421 ◽  
Author(s):  
G Thelander ◽  
F C Kugelberg ◽  
A W Jones
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Pleural Cavity ◽  
Medical Examiner ◽  
Outcome Variable ◽  
Large Uncertainty ◽  
Lung Fluid ◽  
Femoral Blood ◽  
The Mean ◽  
Alternative Specimen

Abstract In connection with medicolegal autopsies peripheral blood (e.g. from a femoral vein) is the specimen of choice for toxicological analysis, although alternative specimens are also sometimes submitted, such as bile, cerebrospinal fluid (CSF), vitreous humor (VH), bladder urine, pleural effusions and/or lung fluid. Ethanol concentrations were determined in duplicate in femoral blood and in various alternative biological specimens by headspace gas chromatography. The analysis was carried out on two different fused silica capillary columns furnishing different retention times for ethanol and both n-propanol and t-butanol were used as internal standards. The results were evaluated by linear regression using blood alcohol concentration (BAC) as dependent or outcome variable and the concentrations in an alternative specimen as independent or predictor variable. The Pearson correlation coefficients were all statistically highly significant (P < 0.001); r = 0.94 (bile), r = 0.98 (CSF), r = 0.97 (VH), r = 0.92 (urine), r = 0.94 (lung fluid) and r = 0.96 (pleural cavity effusions). When the regression model was used to predict femoral BAC from the mean concentration in an alternative specimen the mean and 95% prediction intervals were 1.12 ± 0.824 g/L (bile), 1.41 ± 0.546 g/L (CSF), 1.15 ± 0.42 g/L (VH), 1.29 ± 0.780 g/L (urine), 1.25 ± 0.772 g/L (lung fluid) and 0.68 ± 0.564 g/L (pleural cavity effusions). This large uncertainty for a single new observation needs to be considered when alcohol-related deaths are evaluated and interpreted. However, the analysis of alternative specimens is recommended in medical examiner cases to provide supporting evidence with regard to the origin of ethanol, whether this reflects antemortem (AM) ingestion or postmortem (PM) synthesis.

scholarly journals PENERAPAN REGRESI BINOMIAL NEGATIF UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON

2013 ◽  
Vol 2 (2) ◽  
pp. 6
Author(s):  
PUTU SUSAN PRADAWATI ◽  
KOMANG GDE SUKARSA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Poisson Regression Model ◽  
Response Variable ◽  
Poisson Regression Analysis ◽  
Binomial Regression ◽  
The Mean

Poisson regression was used to analyze the count data which Poisson distributed. Poisson regression analysis requires state equidispersion, in which the mean value of the response variable is equal to the value of the variance. However, there are deviations in which the value of the response variable variance is greater than the mean. This is called overdispersion. If overdispersion happens and Poisson Regression analysis is being used, then underestimated standard errors will be obtained. Negative Binomial Regression can handle overdispersion because it contains a dispersion parameter. From the simulation data which experienced overdispersion in the Poisson Regression model it was found that the Negative Binomial Regression was better than the Poisson Regression model.

Flexible quasi-beta regression models for continuous bounded data

2018 ◽  
Vol 19 (6) ◽  
pp. 617-633 ◽  
Author(s):  
Wagner H Bonat ◽  
Ricardo R Petterle ◽  
John Hinde ◽  
Clarice GB Demétrio
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Beta Regression ◽  
Estimating Function ◽  
Dispersion Parameters ◽  
Linear Predictor ◽  
Mean And Variance ◽  
The Mean ◽  
Bounded Data ◽  
Beta Regression Model

We propose a flexible class of regression models for continuous bounded data based on second-moment assumptions. The mean structure is modelled by means of a link function and a linear predictor, while the mean and variance relationship has the form [Formula: see text], where [Formula: see text], [Formula: see text] and [Formula: see text] are the mean, dispersion and power parameters respectively. The models are fitted by using an estimating function approach where the quasi-score and Pearson estimating functions are employed for the estimation of the regression and dispersion parameters respectively. The flexible quasi-beta regression model can automatically adapt to the underlying bounded data distribution by the estimation of the power parameter. Furthermore, the model can easily handle data with exact zeroes and ones in a unified way and has the Bernoulli mean and variance relationship as a limiting case. The computational implementation of the proposed model is fast, relying on a simple Newton scoring algorithm. Simulation studies, using datasets generated from simplex and beta regression models show that the estimating function estimators are unbiased and consistent for the regression coefficients. We illustrate the flexibility of the quasi-beta regression model to deal with bounded data with two examples. We provide an R implementation and the datasets as supplementary materials.

scholarly journals Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Meng Dun ◽  
Zhicun Xu ◽  
Yan Chen ◽  
Lifeng Wu
Keyword(s):  
Air Quality ◽  
Regression Model ◽  
Hybrid Model ◽  
Air Pollutants ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Air Quality Prediction ◽  
Multivariable Regression Model ◽  
Multivariable Regression ◽  
The Mean

To predict the daily air pollutants, the fractional multivariable model is established. The hybrid model of the grey multivariable regression model with fractional order accumulation model (FGM(0, m)) and support vector regression model (SVR) is used to predict the air pollutants (PM10, PM2.5, and NO2) from December 31, 2018, to January 3, 2019, in Shijiazhuang and Chongqing. The absolute percentage errors (APEs) are used to determine the weights of the FGM(0, m) and SVR. Meanwhile, the Holt–Winters model is used to predict the air quality pollutants for the same location and period. When the mean absolute percent error (MAPE) is 0%–20%, it indicates that the model has good accuracy of fitting and prediction. The MAPE of the hybrid model is less than 20%. It is shown that except for the PM2.5 concentration prediction in Shijiazhuang (13.7%), the MAPE between the forecasting and actual values of the three air pollutants in Shijiazhuang and Chongqing was less than 10%.

scholarly journals Test-day or 305-day milk yield for genetic evaluation of Gir cattle

2019 ◽  
Vol 54 ◽  
Author(s):  
Rodrigo Junqueira Pereira ◽  
Denise Rocha Ayres ◽  
Mário Luiz Santana Junior ◽  
Lenira El Faro ◽  
Aníbal Eugênio Vercesi Filho ◽  
...  
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Breeding Values ◽  
Random Regression Model ◽  
The Mean ◽  
Yield Record ◽  
Genetic Evaluations ◽  
Dairy Breed

Abstract: The objective of this work was to compare genetic evaluations of milk yield in the Gir breed, in terms of breeding values and their accuracy, using a random regression model applied to test-day records or the traditional model (TM) applied to estimates of 305-day milk yield, as well as to predict genetic trends for parameters of interest. A total of 10,576 first lactations, corresponding to 81,135 test-day (TD) records, were used. Rank correlations between the breeding values (EBVs) predicted with the two models were 0.96. The percentage of animals selected in common was 67 or 82%, respectively, when 1 or 5% of bulls were chosen, according to EBVs from random regression model (RRM) or TM genetic evaluations. Average gains in accuracy of 2.7, 3.0, and 2.6% were observed for all animals, cows with yield record, and bulls (sires of cows with yield record), respectively, when the RRM was used. The mean annual genetic gain for 305-day milk yield was 56 kg after 1993. However, lower increases in the average EBVs were observed for the second regression coefficient, related to persistency. The RRM applied to TD records is efficient for the genetic evaluation of milk yield in the Gir dairy breed.

A Large Scale Evaluation of Genetic Variation in Immune and Inflammation Genes and Risk of Non-Hodgkin Lymphoma.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 817-817
Author(s):  
James R. Cerhan ◽  
Stephen M. Ansell ◽  
Zachary S. Fredericksen ◽  
Neil E. Kay ◽  
Mark Liebow ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Genetic Variation ◽  
Regression Model ◽  
Hodgkin Lymphoma ◽  
Candidate Genes ◽  
Mapk Signaling ◽  
Small Scale ◽  
Non Hodgkin Lymphoma ◽  
Individual Snps ◽  
The Mean

Abstract Background: Non-Hodgkin lymphoma (NHL) is a malignancy of lymphocytes, and may develop in the setting of inflammation and immune dysfunction. Small scale evaluations have suggested that common genetic variation in candidate genes related to immune function may predispose to the development of NHL. Here we report a comprehensive analysis of variants within genes associated with immunity and inflammation and risk of NHL. Methods: We used ParAllele’s Immune and Inflammation panel of 9,412 single nucleotide polymorphisms (SNPs) from 1,450 immune/inflammation genes as a discovery tool in a clinic-based study of 458 NHL cases and 484 frequency matched controls seen at the Mayo Clinic from 2002–2005. The panel included 537 coding non-synonymous SNPs (nsSNPs), with the remainder of the SNPs selected as tags from HapMap (tagSNPs) to provide coverage of the candidate genes (r2 = 0.8 and minor allele frequency >0.05). To assess the association of individual SNPs with risk of NHL, we calculated Odds Ratios (ORs) and 95% confidence intervals, adjusted for age and gender. The most prevalent homozygous genotype was used as the reference group, and each polymorphism was modeled individually as having a log-additive effect in the regression model. Associations between haplotypes from each gene and the risk of NHL were calculated using a score test implemented in HAPLO.SCORE. We also modeled the main effects for all independent (r2 <0.25) SNPs from a gene in a multivariate logistic regression model. Results: The mean age at diagnosis was 60 years for cases and 58% were male; in controls the mean age at enrollment was 61.6 years and 55% were male. In the gene analyses, the strongest findings (p≤0.001 from multiple SNP logistic regression or haplotype analysis) were for cAMP responsive element binding protein 1 (CREB1; p=0.0004), fibrinogen alpha chain (FGA; p=0.0006), TNF receptor-associated factor 1 (TRAF1; p=0.001), dual specificity phosphatase 2 (DUSP2; p=0.001), and fibrinogen gamma chain (FGG; p=0.001). In the nsSNP analyses, the strongest findings (p≤0.01) were for integrin β3 (ITGB3) L59P (OR=0.64, 0.50–0.83), Beta-1,3-N-acetylglucosaminyltransferase 3 (B3GNT3) H328R (OR=0.72, 0.57–0.91), transporter 2, ATP-binding cassette (TAP2) T665A (OR=1.32, 1.07–1.63), HLA-B associated transcript 2 (BAT2) V1895L (OR=0.60, 0.42–0.85), and complement component 7 (C7) T587P (OR=1.39, 1.07–1.80). Conclusions. Our results suggest that genetic variability in genes associated with antigen processing (CREB1 and TAP2), lymphocyte trafficking (B3GNT3), immune activation (TRAF1, BAT2), complement and coagulation pathways (FGA, FGG, ITGB3, C7), and MAPK signaling (DUSP2) may be important in the etiology of NHL, and should be pursued in replication studies.

scholarly journals Multi-criteria validation of artificial neural network rainfall-runoff modeling

2009 ◽  
Vol 13 (3) ◽  
pp. 411-421 ◽  
Author(s):  
R. Modarres
Keyword(s):  
Regression Model ◽  
Empirical Model ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Mean And Variance ◽  
High Flows ◽  
The Mean ◽  
Artificial Neural ◽  
Global Statistics ◽  
Non Parametric

Abstract. In this study we propose a comprehensive multi-criteria validation test for rainfall-runoff modeling by artificial neural networks. This study applies 17 global statistics and 3 additional non-parametric tests to evaluate the ANNs. The weakness of global statistics for validation of ANN is demonstrated by rainfall-runoff modeling of the Plasjan Basin in the western region of the Zayandehrud watershed, Iran. Although the global statistics showed that the multi layer perceptron with 4 hidden layers (MLP4) is the best ANN for the basin comparing with other MLP networks and empirical regression model, the non-parametric tests illustrate that neither the ANNs nor the regression model are able to reproduce the probability distribution of observed runoff in validation phase. However, the MLP4 network is the best network to reproduce the mean and variance of the observed runoff based on non-parametric tests. The performance of ANNs and empirical model was also demonstrated for low, medium and high flows. Although the MLP4 network gives the best performance among ANNs for low, medium and high flows based on different statistics, the empirical model shows better results. However, none of the models is able to simulate the frequency distribution of low, medium and high flows according to non-parametric tests. This study illustrates that the modelers should select appropriate and relevant evaluation measures from the set of existing metrics based on the particular requirements of each individual applications.

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