Relevant bibliographies by topics / Logistic Regresion

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Logistic Regresion'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Logistic Regresion"

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Mirwan, Dhea Ramadani, and Muhammad Nuryatno Amin. "PENGARUH FINANCIAL LEVERAGE, PROFITABILITAS, NET PROFIT MARGIN, DAN UKURAN PERUSAHAAN TERHADAP PRAKTIK PERATAAN LABA." AKUNTABILITAS 14, no. 2 (July 28, 2020): 225–42. http://dx.doi.org/10.29259/ja.v14i2.10982.

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The aim of this research is to prove the effect of financial leverage, profitability, net profit margin and firm size to the income smoothing. Population of this research is manufacturing companies listed at the Indonesia Stock Exchange (BEI) for the period of 2016-2018 with sampling determined by purposive sampling. Data analyzed using logistic regression (binary logistic regresion). The results of this research showed that financial leverage and profitability have negative effect to income smoothing, and at the opposite net profit margin has positive effects to income smoothing. Whereas firm size has no effects to income smoothing
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Nuzuliana, Rosmita, Djauhar Ismail, and Hikmah Hikmah. "Hubungan status sosial ekonomi dengan perkembangan batita." Jurnal Kebidanan dan Keperawatan Aisyiyah 12, no. 2 (January 17, 2020): 109–17. http://dx.doi.org/10.31101/jkk.303.

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Abstract: This research aims to determine the relationship socialeconomic status (SES) and other influence factor in the last three monthsdue to child development. We used cross sectional design. SSEindicators were parental level of education, montly family income,parental work . We use identity questionnaires and stimulation ofdevelopment questionnaires, Denver II. Analysis methode by chi-squereand logistic regression for multivariate analysis. Research showed thatthe level of education and occupational status did not affect thedevelopment of toddlers. Affecting factor the development of thetoddlers are parents income, gender, playgroup participation, stimulation.Logistic regresion showed that playgroup participant’s and gender weredominant influence factors for child development.
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Duţă, Irina, Simona Fica, and Daniela Adriana Ion. "The association between insulin resistance and proliferative retinopathy in type 1 diabetes." Romanian Journal Of Internal Medicine 53, no. 3 (September 1, 2015): 261–66. http://dx.doi.org/10.1515/rjim-2015-0034.

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Abstract Introduction. Little is known about the relationship between insulin resistance and proliferative diabetic retinopathy in type 1 diabetes. The aim of this article is to explore the relationship between sight-threatening proliferative diabetic retinopathy and insulin resistance. Methods. This was a cross-sectional study that included 167 type 1 diabetes patients. Insulin resistance was assessed using eGDR (estimated glucose disposal rate) formula. Diabetic retinopathy was assessed by ophthalmoscopy using Early Treatment Diabetic Retinopathy classification. The association between eGDR and proliferative diabetic retinopathy was assessed in uni- and multivariate models using stepwise logistic regression of covariates. The contribution of individual predictors in the final regresion model was examined using Wald statistic. Results. Significantly lower eGDR’s values were observed in patients with proliferative diabetic retinopathy: 5.5 vs. 7 (p = 0.002). The results remained significant (p < 0.001) after adjusting for multiple covariates (sex, diabetes duration, body mass index, HDL cholesterol, LDL cholesterol, triglycerides, smoking). eGDR variable was retained in the final model of stepwise logistic regression (p < 0.001) and showed the strongest association with proliferative diabetic retinopathy (Wald = 12.73). Conclusions. In type 1 diabetes patients insulin resistance was the most important independent risk factor associated with diabetic proliferative retinopathy.
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Elasan, Sadi, and Sıddık Keskin. "An application with multinomial logistic regression analysisMultinomiyal logistik regresyon analizi ile bir uygulama." International Journal of Human Sciences 12, no. 1 (January 26, 2015): 443. http://dx.doi.org/10.14687/ijhs.v12i1.3140.

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Řeháková, Blanka. "Kontrasty v logistické regresi [Contrasts in Logistic Regression]." Czech Sociological Review 44, no. 4 (August 1, 2008): 745–66. http://dx.doi.org/10.13060/00380288.2008.44.4.07.

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Rohmana, Shella Faiz, Agus Rusgiyono, and Sugito Sugito. "PENENTUAN FAKTOR-FAKTOR YANG MEMPENGARUHI INTENSITAS CURAH HUJAN DENGAN ANALISIS DISKRIMINAN GANDA DAN REGRESI LOGISTIK MULTINOMIAL (Studi Kasus: Data Curah Hujan Kota Semarang dari Stasiun Meteorologi Maritim Tanjung Emas Periode Oktober 2018 – Maret 2019)." Jurnal Gaussian 8, no. 3 (August 30, 2019): 398–406. http://dx.doi.org/10.14710/j.gauss.v8i3.26684.

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Meteorologist develop rainfall forecasting methods to obtain better and more accurate rainfall information. One of them is the research of grid data and the method of grouping rainfall. According to BMKG, rainfall is classified into light, medium, and heavy rain. This study aims to determine the factors that influencing rainfall grouping using multiple discriminant analysis with a stepwise selection method. This study uses the daily climate data of Semarang City for period of October 2018 to March 2019. Based on its partial F value, the wind speed variable is eliminated so the significant variable on rainfall grouping are air temperature, air humidity, and wind direction. This analysis produces discriminant scores obtained from linear combinations between discriminant weights and observation values of significant independent variable. The classification procedure is based on the discriminant score each observations compared to cutting score resulted in classification accuracy of 62.89%. Multinomial logistic regression analysis is used to determine the effect of independent variables on rainfall intensity using the odds ratio. This analysis produces an estimate of the conditional probability of each group using significant independent variables are air temperature, air humidity, wind speed, and wind direction. The classification procedure is based on the largest conditional probability value between rainfall groups resulted in classification accuracy of 69.80%. Keywords: multiple discriminant analysis, multinomial logistic regresion, classification accuracy, rainfall
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Negreiros-Fransozo, Maria Lucia, and Augusto Flores. "ALLOMETRY OF THE SECONDARY SEXUAL CHARACTERS OF THE SHORE CRAB PACHYGRAPSUS TRANSVERSUS (GIBBES, 1850) (BRACHYURA, GRAPSIDAE)." Crustaceana 72, no. 9 (1999): 1051–66. http://dx.doi.org/10.1163/156854099504013.

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AbstractThe allometric growth of secondary sexual characters in Pachygrapsus transversus is investigated from the 2nd crab stage onward. Clear sexual dimorphism is restricted to abdominal morphology, but ANCOVA analyses showed that chelae become larger in males and the carapace becomes wider in females. Size at the puberty moult in both sexes was estimated using Somerton's computer techniques. Mature II analyses applied to bi-log gonopod length vs. carapace length relationships indicated a puberty moult at 5.0 mm in males. In females, Mature I analyses detected the overlapping growth phase lines in bi-log carapace length vs. abdomen width scatterplots. Fitting the logistic equation provided an estimate of 50% maturity at 5.5 mm. The regression lines separate young and resting individuals from the potentially reproductive females, but they do not separate young from adult crabs. Year-round monthly samples showed that the proportion of small adult-like females is higher during the breeding season. After breeding, females may moult to a young-like morphotype, as observed in controlled laboratory conditions. Moulting to a resting condition splits smaller mature females into different "growth phase" lines. Therefore, estimates of female size at sexual maturity by means of abdomen allometric growth analyses are inadequate in this species. El crecimiento alometrico de los caracteres sexuales secundarios de Pachygrapsus transversus ha sido investigado desde el segundo estadio pos-larval. El dimorfismo abdominal es la unica caracterostica que permite distinguir claramente los sexos, pero los analisis de covarianza aplicados a las regresiones obtenidas demuestran que las quelas se vuelven m as grandes en machos y que el caparazon se vuelve mas ancho en hembras. La talla en que ambos sexos alcanzan la maturidad sexual ha sido estimada atraves de las tecnicas de Somerton. La aplicacion del programa Mature II a la regresion bi-log entre el largo del caparazon y el largo del gonopodo indica que la muda de la pubertad de los machos ocurre a los 5,0 mm. En las hembras, el programa Mature I detecto la sobreposicion de loneas de regresi on en los diagramas de dispersion de la regresion bi-log entre el largo del caparazon y el ancho del abdomen. Del ajuste de la ecuacion logostica se obtiene que 50% de las hembras son sexualmente adultas a los 5,5 mm. Estas rectas de regresion separan las hembras con potencial reproductivo de los indivoduos j ovenes y aquellos en reposo sexual. Sin embargo, las rectas no separan jovenes de adultos. Muestreos mensuales a lo largo de un ano indican que la proporcion de hembras de talla puberal con morfologia adulta es mas elevada durante el peroodo reproductivo. Despues de la reproduccion, estas hembras pueden obtener en la siguiente muda una condicion morfologica semejante a la joven, como fue verificado en condiciones de laboratorio controladas. La obtencion del reposo sexual divide a las hembras de talla puberal en rectas de regresion distintas, por lo que las estimativas de talla a la maturidad sexual atraves del analisis del crecimiento alometrico del abdomen son inadequadas para esta especie.
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Sulistyani, Sulistyani, and Deny Ismanto. "ANALISIS RASIO KEUANGAN UNTUK MEMPREDIKSI KONDISI FINANCIAL DISTRESS PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BEI." Jurnal Fokus Manajemen Bisnis 7, no. 2 (February 13, 2020): 156. http://dx.doi.org/10.12928/fokus.v7i2.1744.

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Financial distress precedes bankruptcy. Most financial distress models actually rely on bankruptcy data, which is easier to obtain. The purpose of this research to examine financial ratios that predict financial distress condition of a firm. The sample of this research consist of 14 distress firm and 79 non-distress firms, chosen by purposive sampling. The statistic method which is used to test on the research hypothesis is logistic regresion. The results show that the liquidity ratio (current assets/current liabilities) and a leverage ratio (current leabilities/total asset) is a significant variable to determine of financial distress firms. When profitability ratio (net income/net sales) and price earning ratio (market price per share/earnings per share) are not significant variables to determine of financial distress.
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Pir Bavaghar, M. "Deforestation modelling using logistic regression and GIS." Journal of Forest Science 61, No. 5 (June 3, 2016): 193–99. http://dx.doi.org/10.17221/78/2014-jfs.

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Indriyanto, Jatmiko, Miftakhul Huda, and Ida Afriliana. "Pengembangan Algoritma C4.5 Berbasis Particle Swarm Optimization untuk Penentuan Kelayakan Asuransi." Jurnal Health Sains 2, no. 3 (March 23, 2021): 427–37. http://dx.doi.org/10.46799/jsa.v2i3.204.

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Tujuan penelitian ini adalah untuk Pengembangan Algoritma C4.5 Berbasis Particle Swarm Optimization Untuk Penentuan Kelayakan Asuransi. metode logistic regresion, decision trees, k-nearest neighbors, naïve bayes dan support vector machines. Model tersebut akan menentukan atau memprediksi status konsumen dimasa mendatang. Observasi yang mirip juga pernah dilakukan, tetapi dengan cara berbeda. Pada penilitian ini, akan digunakan algoritma klasifikasi C4.5 berbasis Particle Swarm Optimization (PSO), hasil ketepatan yang diinginkan lebih bagus dibandingkan hanya memakai algoritma C4.5 untuk mengatasi permasalahan pada kasus pemilihan produk asuransi. dapat disimpulkan bahwa nilai akurasi yang didapatkan pada model algoritma C4.5 berbasis PSO adalah 98.93% lebih bagus jika dibandingkan dengan model algoritma C4.5 yaitu 97.84%. Dari hasil tersebut didapatkan perbedaan antara kedua model yaitu senilai 0.4%. Selagi untuk penelaahan menggunakan ROC curve bagi kedua model ialah,untuk model algoritma C4.5 nilai AUC adalah 0.970 dengan urutan diagnosa Excellent Classification, dan untuk model algoritma C4.5 berbasis PSO nilai AUC adalah 0.968 dengan urutan diagnosa Excellent Classification.
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Dissertations / Theses on the topic "Logistic Regresion"

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Kazemi, Seyed Mehran. "Relational logistic regression." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50091.

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Aggregation is a technique for representing conditional probability distributions as an analytic function of parents. Logistic regression is a commonly used representation for aggregators in Bayesian belief networks when a child has multiple parents. In this thesis, we consider extending logistic regression to directed relational models, where there are objects and relations among them, and we want to model varying populations and interactions among parents. We first examine the representational problems caused by population variation. We show how these problems arise even in simple cases with a single parametrized parent, and propose a linear relational logistic regression which we show can represent arbitrary linear (in population size) decision thresholds, whereas the traditional logistic regression cannot. Then we examine representing interactions among the parents of a child node, and representing non-linear dependency on population size. We propose a multi-parent relational logistic regression which can represent interactions among parents and arbitrary polynomial decision thresholds. We compare our relational logistic regression to Markov logic networks and represent their analogies and differences. Finally, we show how other well-known aggregators can be represented using relational logistic regression.
Science, Faculty of
Computer Science, Department of
Graduate
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Widman, Linnea. "Regression då data utgörs av urval av ranger." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-60664.

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För alpina skidåkare mäter man prestationer i så kallad FIS-ranking. Vi undersöker några metoder för hur man kan analysera data där responsen består av ranger som dessa. Vid situationer då responsdata utgörs av urval av ranger finns ingen självklar analysmetod. Det vi undersöker är skillnaderna vid användandet av olika regressionsanpassningar så som linjär, logistisk och ordinal logistisk regression för att analysera data av denna typ. Vidare används bootstrap för att bilda konfidensintervall. Det visar sig att för våra datamaterial ger metoderna liknande resultat när det gäller att hitta betydelsefulla förklarande variabler. Man kan därmed utgående från denna undersökning, inte se några skäl till varför man ska använda de mer avancerade modellerna.
Alpine skiers measure their performance in FIS ranking. We will investigate some methods on how to analyze data where response data is based on ranks like this. In situations where response data is based on ranks there is no obvious method of analysis. Here, we examine differences in the use of linear, logistic and ordinal logistic regression to analyze data of this type. Bootstrap is used to make confidence intervals. For our data these methods give similar results when it comes to finding important explanatory variables. Based on this survey we cannot see any reason why one should use the more advanced models.
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Noreika, Marius. "Mirtingumo nuo galvos smegenų insulto prognozavimo modeliai ir programinės priemonės." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2007. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_143821-99512.

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Mirtingumo nuo įvairių ligų įvertinimas ir prognozavimas pagal atlikto tyrimo duomenis – dažnas statistinės analizės uždavinys medicinoje. Juose siekiama prognozuoti tikėtiną mirčių nuo tiriamos ligos skaičių, susirgimo tam tikra liga tikimybę ar išskirti rizikos grupes, įvertinant tyrimo metu surinktų stebimos populiacijos imties kintamųjų duomenis ir nustatant, kokia priklausomybę juos sieja. Pagrindiniai šio darbo tikslai: susipažinti su statistikos metodais, taikomais mirtingumo duomenų analizei; sudaryti statistinės analizės modelius turimiems mirtingumo duomenims; realizuoti sudarytus modelius programiškai, panaudojant SAS sistemą ir SAS makro programavimo galimybes. Panaudojus Puasono, logistinės ir Kokso regresin��s analizės metodus sudaryti mirtingumo nuo galvos smegenų insulto (GSI) prognozavimo modeliai. Sudaryti modeliai realizuoti programiškai, panaudojus SAS programavimo kalbą, SAS/IML posistemės galimybes ir SAS makro programavimo priemones. Sukurti regresinės analizės modeliai ir programines priemonės panaudotos Kauno medicinos universiteto Kardiologijos instituto 1980-2004 metais atliktų tyrimų metu surinktų Kauno miesto 25-64 m. amžiaus gyventojų mirtingumo nuo GSI duomenų analizei atlikti.
Estimation and forecasting of mortality from various diseases are very frequent data analysis tasks in medicine nowadays. In order to estimate expected number of deaths, probability to die from a disease or trends in mortality we should apply the most suitable statistical methods. Data analysis models were created using Poisson, logistic, Cox regression methods and realized in SAS macros. Created software also contains models for goodness of fit analysis, graphical visualization and prepares a report of data analysis in RTF (Rich Text Format) format. Analysis was made for mortality from stroke data among Kaunas population aged 25 to 64 during the period 1980-2004. The study contains the description of applying created data analysis models, SAS macros and received results.
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Nargis, Suraiya, and n/a. "Robust methods in logistic regression." University of Canberra. Information Sciences & Engineering, 2005. http://erl.canberra.edu.au./public/adt-AUC20051111.141200.

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My Masters research aims to deepen our understanding of the behaviour of robust methods in logistic regression. Logistic regression is a special case of Generalized Linear Modelling (GLM), which is a powerful and popular technique for modelling a large variety of data. Robust methods are useful in reducing the effect of outlying values in the response variable on parameter estimates. A literature survey shows that we are still at the beginning of being able to detect extreme observations in logistic regression analyses, to apply robust methods in logistic regression and to present informatively the results of logistic regression analyses. In Chapter 1 I have made a basic introduction to logistic regression, with an example, and to robust methods in general. In Chapters 2 through 4 of the thesis I have described traditional methods and some relatively new methods for presenting results of logistic regression using powerful visualization techniques as well as the concepts of outliers in binomial data. I have used different published data sets for illustration, such as the Prostate Cancer data set, the Damaged Carrots data set and the Recumbent Cow data set. In Chapter 4 I summarize and report on the modem concepts of graphical methods, such as central dimension reduction, and the use of graphics as pioneered by Cook and Weisberg (1999). In Section 4.6 I have then extended the work of Cook and Weisberg to robust logistic regression. In Chapter 5 I have described simulation studies to investigate the effects of outlying observations on logistic regression (robust and non-robust). In Section 5.2 I have come to the conclusion that, in the case of classical or robust multiple logistic regression with no outliers, robust methods do not necessarily provide more reasonable estimates of the parameters for the data that contain no st~ong outliers. In Section 5.4 I have looked into the cases where outliers are present and have come to the conclusion that either the breakdown method or a sensitivity analysis provides reasonable parameter estimates in that situation. Finally, I have identified areas for further study.
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Rashid, Mamunur. "Inference on Logistic Regression Models." Bowling Green State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1214165101.

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Brabcová, Hana. "Využití logistické regrese ve výzkumu trhu." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-19234.

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The aim of this work is to decide the real usage of logistic regression in the market research tasks respecting the needs of final users of research results. The main argument for the final decision is the comparison of its output to the output of an alternative classification method used in practice -- a classification tree method. The topic is divided into three parts. The first part describes the theoretical framework and approaches linked to logistic regression (chapter 2 and 3). The second part analyses the experience with the usage of logistic regression in Czech market research companies (chapter 4) and the topic is closed by applying the method on real data and comparing the output to the classification tree output (chapter 5 and 6).
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Williams, Ulyana P. "On Some Ridge Regression Estimators for Logistic Regression Models." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3667.

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The purpose of this research is to investigate the performance of some ridge regression estimators for the logistic regression model in the presence of moderate to high correlation among the explanatory variables. As a performance criterion, we use the mean square error (MSE), the mean absolute percentage error (MAPE), the magnitude of bias, and the percentage of times the ridge regression estimator produces a higher MSE than the maximum likelihood estimator. A Monto Carlo simulation study has been executed to compare the performance of the ridge regression estimators under different experimental conditions. The degree of correlation, sample size, number of independent variables, and log odds ratio has been varied in the design of experiment. Simulation results show that under certain conditions, the ridge regression estimators outperform the maximum likelihood estimator. Moreover, an empirical data analysis supports the main findings of this study. This thesis proposed and recommended some good ridge regression estimators of the logistic regression model for the practitioners in the field of health, physical and social sciences.
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Li, Yin. "Application of logistic regression in biostatistics." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=68201.

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The primary objective of this paper is a focused introduction to the logistic regression model and its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariates. The approach we will take is to develop the model from a regression analysis point of view. Also in this paper, an estimator of the common odds ratio in one-to-one matched case-control studies is proposed. The connection between this estimator and the James-Stein estimating procedure is highlighted through the argument of estimating functions. Comparisons are made between this estimator, the conditional maximum likelihood estimator, and the estimator ignoring the matching.
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Mak, Carmen. "Polychotomous logistic regression via the Lasso." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0004/NQ41227.pdf.

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Al-Sarraf, Z. J. "Some problems connected with logistic regression." Thesis, Brunel University, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.374301.

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Books on the topic "Logistic Regresion"

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Menard, Scott W. Logistic regression. Thousand Oaks, Calif: Sage Publications, 2009.

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Logistic regression. Thousand Oaks, Calif: Sage Publications, 2009.

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Kleinbaum, David G. Logistic Regression. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7.

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Kleinbaum, David G., and Mitchel Klein. Logistic Regression. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1742-3.

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Pampel, Fred. Logistic Regression. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2000. http://dx.doi.org/10.4135/9781412984805.

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Logistic regression: From introductory to advanced concepts and applications. Thousand Oaks, Calif: Sage Publications, 2010.

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Stanley, Lemeshow, ed. Applied logistic regression. New York: Wiley, 1989.

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Stanley, Lemeshow, ed. Applied logistic regression. 2nd ed. New York: Wiley, 2000.

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Hosmer, David W., Stanley Lemeshow, and Rodney X. Sturdivant. Applied Logistic Regression. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118548387.

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Hosmer, David W., and Stanley Lemeshow. Applied Logistic Regression. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2000. http://dx.doi.org/10.1002/0471722146.

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Book chapters on the topic "Logistic Regresion"

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Kleinbaum, David G. "Introduction to Logistic Regression." In Logistic Regression, 1–38. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_1.

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Kleinbaum, David G. "Important Special Cases of the Logistic Model." In Logistic Regression, 39–72. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_2.

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Kleinbaum, David G. "Computing the Odds Ratio in Logistic Regression." In Logistic Regression, 73–99. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_3.

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Kleinbaum, David G. "Maximum Likelihood Techniques: An Overview." In Logistic Regression, 101–24. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_4.

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Kleinbaum, David G. "Statistical Inferences Using Maximum Likelihood Techniques." In Logistic Regression, 125–60. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_5.

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Kleinbaum, David G. "Modeling Strategy Guidelines." In Logistic Regression, 161–89. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_6.

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Kleinbaum, David G. "Modeling Strategy for Assessing Interaction and Confounding." In Logistic Regression, 191–226. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_7.

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Kleinbaum, David G. "Analysis of Matched Data Using Logistic Regression." In Logistic Regression, 227–51. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_8.

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Holmes, William H., and William C. Rinaman. "Logistic Regression." In Statistical Literacy for Clinical Practitioners, 397–422. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12550-3_15.

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Sheather, Simon J. "Logistic Regression." In Springer Texts in Statistics, 263–303. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-09608-7_8.

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Conference papers on the topic "Logistic Regresion"

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Imamah and Fika Hastarita Rachman. "Twitter Sentiment Analysis of Covid-19 Using Term Weighting TF-IDF And Logistic Regresion." In 2020 6th Information Technology International Seminar (ITIS). IEEE, 2020. http://dx.doi.org/10.1109/itis50118.2020.9320958.

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Lv, Cui, and Di-Rong Chen. "Interpretable Functional Logistic Regression." In the 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3207677.3277962.

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Yun, Woo-han, Do-Hyung Kim, Su-young Chi, and Ho-Sub Yoon. "Two-Dimensional Logistic Regression." In 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007). IEEE, 2007. http://dx.doi.org/10.1109/ictai.2007.48.

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Lubenko, Ivans, and Andrew D. Ker. "Steganalysis using logistic regression." In IS&T/SPIE Electronic Imaging, edited by Nasir D. Memon, Jana Dittmann, Adnan M. Alattar, and Edward J. Delp III. SPIE, 2011. http://dx.doi.org/10.1117/12.872245.

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Cui, Zhicheng, Muhan Zhang, and Yixin Chen. "Deep Embedding Logistic Regression." In 2018 IEEE International Conference on Big Knowledge (ICBK). IEEE, 2018. http://dx.doi.org/10.1109/icbk.2018.00031.

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Isaac, Jackson, and Sandhya Harikumar. "Logistic regression within DBMS." In 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2016. http://dx.doi.org/10.1109/ic3i.2016.7918045.

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Chen, Wenlin, Yixin Chen, Yi Mao, and Baolong Guo. "Density-based logistic regression." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2487583.

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Liu, Fanghui, Xiaolin Huang, and Jie Yang. "Indefinite Kernel Logistic Regression." In MM '17: ACM Multimedia Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3123266.3123295.

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van Erp, N., and P. van Gelder. "Bayesian logistic regression analysis." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2013. http://dx.doi.org/10.1063/1.4819994.

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Bryg, David J., George Mink, and Link C. Jaw. "Combining Lead Functions and Logistic Regression for Predicting Failures on an Aircraft Engine." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50118.

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The increasing demand for performance and durability of advanced aerospace systems has increased the need for health management of these systems. Effective health management involves seamless integration of failure diagnostics, failure prediction, part life estimation, and maintenance logistics. These capabilities have only partially been implemented in current health management systems. Hence the effectiveness of current management systems has not achieved its potential. To achieve the goal of effective prognostic and health management (PHM), promising technologies from various disciplines must be integrated. One of these technologies is logistic regression. In this method, aircraft engine takeoff data is combined with control system fault information and by introducing lead times prior to the fault. Lead times of 1, 7, 14, and 30 days were analyzed using logistic regression on the difference from expected thermodynamic values. The resulting equations give probability of failure over time. An example using real engine data from GE-F414 engines to predict engine stall and anti-ice valve failures are presented. The results show good predictability of these events between a week and a month in advance. For example, for the event of an imminent anti-ice valve failure, the true-negative fraction was 99.6% and the positive-predictive value was 93.1%. This methodology can be combined with an engine health monitoring (EHM) system to provide prognostic failure predictions. Receiver Operator Characteristic (ROC) curves were evaluated as an additional measure of the quality of the predictions. These ROC curves show that there is prognostic value with this approach. This methodology can be updated and refined with additional data. As the results get more refined, the reliability of the fleet can increase, costs can be reduced, and safety increased.
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Reports on the topic "Logistic Regresion"

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Bai, Z. D., P. R. Krishnaiah, and L. C. Zhao. Variable Selection in Logistic Regression. Fort Belvoir, VA: Defense Technical Information Center, June 1987. http://dx.doi.org/10.21236/ada186032.

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BG Amindan and DN Hagedorn. Logistic Regression Applied to Seismic Discrimination. Office of Scientific and Technical Information (OSTI), October 1998. http://dx.doi.org/10.2172/1360.

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Graham, Bryan. Sparse Network Asymptotics for Logistic Regression. Cambridge, MA: National Bureau of Economic Research, October 2020. http://dx.doi.org/10.3386/w27962.

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Stefanski, L. A., and R. J. Carroll. Covariate Measurement Error in Logistic Regression. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada160277.

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Churchill, Alexandrea, and Grace Kissling. Convergence in Mixed Effects Logistic Regression Models. Journal of Young Investigators, February 2019. http://dx.doi.org/10.22186/jyi.36.2.18-35.

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Buttrey, Samuel E. The Smarter Regression" Add-In for Linear and Logistic Regression in Excel". Fort Belvoir, VA: Defense Technical Information Center, July 2007. http://dx.doi.org/10.21236/ada470645.

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Belloni, Alexandre, Victor Chernozhukov, and Ying Wei. Honest confidence regions for a regression parameter in logistic regression with a large number of controls. Institute for Fiscal Studies, December 2013. http://dx.doi.org/10.1920/wp.cem.2013.6713.

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Fraser, R., R. Fernandes, and R. Latifovic. Multi-temporal Burned area Mapping Using Logistic Regression Analysis and Change Metrics. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219870.

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Stefanski, L. A., R. J. Carroll, and D. Ruppert. Optimally Bounded Score Functions for Generalized Linear Models with Applications to Logistic Regression. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada160348.

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Haubrich, Julia, Sarah Benz, Ullrich Isermann, Beat Schäffer, Rainer Schmid, Dirk Schreckenberg, Jean Marc Wunderli, and Rainer Guski. Leq+X - Lärmexposition, Ereignishäufigkeiten und Belästigung: Re-Analyse von Daten zur Belästigung und Schlafstörung durch Fluglärm an deutschen und Schweizer Flughäfen. Universitätsbibliothek der Ruhr-Universität Bochum, 2020. http://dx.doi.org/10.46586/rub.164.139.

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In this study, part of the data sets from 4 large Swiss and German aircraft noise impact studies are re-analysed using logistic multi-level regression models. The aim is to investigate the assumptions that the prediction of a) the percentage of persons highly annoyed by aircraft noise or b) the percentage of persons highly sleep disturbed by aircraft noise can be improved if (i) instead of the energy-equivalent continuous noise level alone, either additional or alternative, more frequency-based aircraft noise metrics and (ii) also airport-specific characteristics are used as predictors. The results support both assumptions; both regarding the percentage of persons highly annoyed and regarding the percentage of persons highly sleep disturbed.
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