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Estimating collection size with logistic regression

Jingfang Xu, Sheng Wu, Xing Li
2007 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07  
In uncooperative environments, collection size estimation algorithms are adopted to estimate the sizes of collections with their search interfaces.  ...  This paper proposes heterogeneous capture (HC) algorithm, in which the capture probabilities of documents are modeled with logistic regression.  ...  Using covariates, HC algorithm models the capture probabilities with logistic regression, and estimates collection size through conditional maximum likelihood.  ... 
doi:10.1145/1277741.1277910 dblp:conf/sigir/XuWL07 fatcat:ckzjwzctnvg7hgcgswzak7wd2a

Supplemental Material, Methods_supplement - Mental Illness, the Media, and the Moral Politics of Mass Violence: The Role of Race in Mass Shootings Coverage

Scott W. Duxbury, Laura C. Frizzell, Sadé L. Lindsay
2018 Figshare  
Nevertheless, we estimate a multilevel model as well as a logistic regression with clustered standard errors to highlight the robustness of the results.  ...  Thus, the rare events approach first estimates a logistic regression and then subtracts the bias in parameter vector to yield bias-corrected regression estimates.  ... 
doi:10.25384/sage.6814820 fatcat:szarkdk3dffcbbuju5au26phsa

Cox proportional hazards models have more statistical power than logistic regression models in cross-sectional genetic association studies

Jeroen B van der Net, A Cecile J W Janssens, Marinus J C Eijkemans, John J P Kastelein, Eric J G Sijbrands, Ewout W Steyerberg
2008 European Journal of Human Genetics  
We applied Cox proportional hazards models and logistic regression models, and compared effect estimates (hazard ratios and odds ratios) and statistical power.  ...  Absolute differences in power did depend on the effect estimate, genotype frequency and sample size, and were most prominent for genotypes with minor effects.  ...  With the term 'effect estimate', we refer to hazard ratios in the Cox proportional hazards models and odds ratios in the logistic regression models.  ... 
doi:10.1038/ejhg.2008.59 pmid:18382476 fatcat:wuc6z42jlbcezaucrhult2mgoa

Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data

Mohamad Adam Bujang, Nadiah Sa'at, Tg Mohd Ikhwan Tg Abu Bakar Sidik, Lim Chien Joo
2018 Malaysian Journal of Medical Sciences  
This study aims to propose sample size guidelines for logistic regression based on observational studies with large population.  ...  Different study designs and population size may require different sample size for logistic regression.  ...  According to the paper, adjustment needed to be made for the sample size tables such as dividing the estimated sample size with a factor of (1-p 2 ) when sample size need to be estimated for logistic regression  ... 
doi:10.21315/mjms2018.25.4.12 pmid:30914854 pmcid:PMC6422534 fatcat:z5hsw65pfjdsnl4hu53hor73gq

An Explorative Study on Estimating Local Accuracies in Land-Cover Information Using Logistic Regression and Class-Heterogeneity-Stratified Data

Jingxiong Zhang, Wenjing Yang, Wangle Zhang, Yu Wang, Di Liu, Yingchang Xiu
2018 Remote Sensing  
Local or per-pixel accuracy is usually estimated through empirical modelling, such as logistic regression, which often proceeds in a class-aggregated or a class-stratified way, with the latter being generally  ...  Candidate explanatory variables for logistic regression included sample pixels' map classes, positions, and contextual features that were computed in different-sized moving windows.  ...  Logistic-regression-kriging can thus be viewed as kriging with local means to get estimation of I(x), with logistic regression predicting local means, while kriging transferring spatial information contained  ... 
doi:10.3390/rs10101581 fatcat:nntqmwrxanabjogrxtehp4z7wi

Multiple Observers Ranked Set Samples for Shrinkage Estimators [article]

Andrew David Pearce, Armin Hatefi
2021 arXiv   pre-print
regression and logistic regression.  ...  Through extensive numerical studies, we show that shrinkage methods with the multi-observer RSS result in more efficient coefficient estimates.  ...  Logistic Regression Estimators with RSS In this section, we investigate the use of MRS data for shrinkage estimators of logistic regression.  ... 
arXiv:2110.07851v1 fatcat:elqnkmzxundxhpuzmemtacq44a

Logistic Regression Under Sparse Data Conditions

David A. Walker, Thomas J. Smith
2020 Journal of Modern Applied Statistical Methods  
estimation bias.  ...  Results indicated sparseness in binary predictors introduces bias that is substantial with small sample sizes, and the Firth procedure can effectively correct this bias.  ...  Table 3 are descriptive statistics for the estimated regression parameters from the logistic regression model fitted to data of size N = 100, using one binary predictor with sparseness = 5% (i.e., the  ... 
doi:10.22237/jmasm/1604190660 fatcat:5arybzb4drhkdfrwakbsimjg54

Analyzing establishment nonresponse using an interpretable regression tree model with linked administrative data

Polly Phipps, Daniell Toth
2012 Annals of Applied Statistics  
Testing the model on a disjoint set of establishment data with a very large sample size (n=179,360) offers evidence that the regression tree model accurately describes the association between the establishment  ...  The accuracy of this modeling approach is compared to that of logistic regression through simulation.  ...  APPENDIX: SIMULATIONS TO COMPARE REGRESSION TREE AND LOGISTIC REGRESSION MODELING PROCEDURES In this section we compare the performance of the regression tree modeling to the more common logistic regression  ... 
doi:10.1214/11-aoas521 fatcat:ubr3ctcj4rdjtkjjoy5uxufrwa

A Solution to Separation and Multicollinearity in Multiple Logistic Regression

Jianzhao Shen, Sujuan Gao
2008 Journal of Data Science  
The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other.  ...  However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large  ...  We thank Dr.Shelley Bull for providing us with a GAUSS program for comparison with our program.  ... 
pmid:20376286 pmcid:PMC2849171 fatcat:wl4d6znu6za77mhhuur4626lde

Credit Risk Modeling for Commercial Banks

2014 International Journal of Academic Research in Accounting, Finance and Management Sciences  
The aim of this paper is to examine the efficiency of two credit risk modeling (CRM) to predict the credit risk of commercial Iranian banks: (1) Logistic regression model (LRM); (2) Artificial neural networks  ...  Results of this study show that logistic regression model has high accuracy in estimating good customers compared with ANN model and also ANN models in a comparison with LRM has high accuracy in estimating  ...  This is equivalent and estimated by applying a logistic regression to issuer-year of data.  ... 
doi:10.6007/ijarafms/v4-i3/1181 fatcat:xchdviy3kbdc7cz3yh4hx6qmhe

Incorporating Survey Weights into Binary and Multinomial Logistic Regression Models

Kennedy Sakaya Barasa
2015 Science Journal of Applied Mathematics and Statistics  
Quasi-likelihood maximization is the method that is used to make estimation with the adjusted weights but the other new method that can be created is correct likelihood for logistic regression which included  ...  Analysis: Both binary logistic regression model and multinomial logistic regression model were used in parameter estimation and we applied the methods to body mass index data from Nairobi Hospital, which  ...  When we estimate binary logistic regression coefficients, we multiply sampling weights with Multinomial Logistic Regression Models both covariates and intercepts to create new covariates and new intercepts  ... 
doi:10.11648/j.sjams.20150306.13 fatcat:rcbnagctavbhtn7523ctcvxfsa

Using Historical In-Process and Product Metrics for Early Estimation of Software Failures

Nachiappan Nagappan, Thomas Ball, Brendan Murphy
2006 Proceedings - International Symposium on Software Reliability Engineering  
The benefits that a software organization obtains from estimates of product quality are dependent upon how early in the product cycle that these estimates are available.  ...  Early estimation of software quality can help organizations make informed decisions about corrective actions.  ...  We also plan to replicate our study with other Microsoft products like SQL Server, Microsoft OFFICE etc.  ... 
doi:10.1109/issre.2006.50 dblp:conf/issre/NagappanBM06 fatcat:6zlpuummxjgmbozd7kksk2wqfy

Investigating the Source of a Disease Outbreak Based on Risk Estimation: A Simulation Study Comparing Risk Estimates Obtained From Logistic and Poisson Regression Applied to a Dichotomous Outcome

Chanapong Rojanaworarit, Jason J. Wong
2019 Ochsner Journal  
The purpose of this study was to empirically compare risk estimates obtained from logistic regression and Poisson regression with robust standard errors in terms of effect size and determination of the  ...  Conclusion: Poisson regression with robust standard errors proved to be a decisive and consistent method to estimate risk associated with a single source in an outbreak when the cohort data collection  ...  All the CIs estimated by the univariable Poisson regression with robust standard errors were narrower than those estimated by the univariable logistic regression (Table 2) .  ... 
doi:10.31486/toj.18.0166 pmid:31528132 pmcid:PMC6735601 fatcat:quovvuhhxnesbfnzkxkkwiubb4

Prediction of number of cases expected and estimation of the final size of coronavirus epidemic in India using the logistic model and genetic algorithm [article]

Ganesh Kumar M, Soman K.P, Gopalakrishnan E.A, Vijay Krishna Menon, Sowmya V
2020 arXiv   pre-print
In this paper, we have applied the logistic growth regression model and genetic algorithm to predict the number of coronavirus infected cases that can be expected in upcoming days in India and also estimated  ...  the final size and its peak time of the coronavirus epidemic in India.  ...  Acknowledgements Code from the paper by Milan Batista [1] is used for obtaining the plots of logistic model.  ... 
arXiv:2003.12017v1 fatcat:aomnectzujd2dhh7tln2mdxljy

Genotype distribution-based inference of collective effects in genome-wide association studies: insights to age-related macular degeneration disease mechanism

Hyung Jun Woo, Chenggang Yu, Kamal Kumar, Bert Gold, Jaques Reifman
2016 BMC Genomics  
logistic regression.  ...  We compared pairwise tests and collective inference methods, the latter based both on DDA and logistic regression.  ...  It has been estimated that, for continuous variables, the accuracy of logistic regression models can be lower by ∼ 30 % than that of discriminant analyses for a given sample size [35, 37] .  ... 
doi:10.1186/s12864-016-2871-3 pmid:27576376 pmcid:PMC5006276 fatcat:hjcn7nw2zbcxfa2nj32buqvqri
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