Retro-regression--another important multivariate regression improvement.
Randić, M
2001-01-01
We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.
Variable importance in latent variable regression models
Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.
2014-01-01
The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable
Using Dominance Analysis to Determine Predictor Importance in Logistic Regression
Azen, Razia; Traxel, Nicole
2009-01-01
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Spontaneous regression of metastases from melanoma: review of the literature
DEFF Research Database (Denmark)
Kalialis, Louise Vennegaard; Drzewiecki, Krzysztof T; Klyver, Helle
2009-01-01
Regression of metastatic melanoma is a rare event, and review of the literature reveals a total of 76 reported cases since 1866. The proposed mechanisms include immunologic, endocrine, inflammatory and metastatic tumour nutritional factors. We conclude from this review that although the precise...
Interpreting Multiple Linear Regression: A Guidebook of Variable Importance
Nathans, Laura L.; Oswald, Frederick L.; Nimon, Kim
2012-01-01
Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights, often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what…
Relative Importance for Linear Regression in R: The Package relaimpo
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Ulrike Gromping
2006-09-01
Full Text Available Relative importance is a topic that has seen a lot of interest in recent years, particularly in applied work. The R package relaimpo implements six different metrics for assessing relative importance of regressors in the linear model, two of which are recommended - averaging over orderings of regressors and a newly proposed metric (Feldman 2005 called pmvd. Apart from delivering the metrics themselves, relaimpo also provides (exploratory bootstrap confidence intervals. This paper offers a brief tutorial introduction to the package. The methods and relaimpo’s functionality are illustrated using the data set swiss that is generally available in R. The paper targets readers who have a basic understanding of multiple linear regression. For the background of more advanced aspects, references are provided.
Relative Importance for Linear Regression in R: The Package relaimpo
Groemping, Ulrike
2006-01-01
Relative importance is a topic that has seen a lot of interest in recent years, particularly in applied work. The R package relaimpo implements six different metrics for assessing relative importance of regressors in the linear model, two of which are recommended - averaging over orderings of regressors and a newly proposed metric (Feldman 2005) called pmvd. Apart from delivering the metrics themselves, relaimpo also provides (exploratory) bootstrap confidence intervals. This paper offers a b...
Important Literature in Endocrinology: Citation Analysis and Historial Methodology.
Hurt, C. D.
1982-01-01
Results of a study comparing two approaches to the identification of important literature in endocrinology reveals that association between ranking of cited items using the two methods is not statistically significant and use of citation or historical analysis alone will not result in same set of literature. Forty-two sources are appended. (EJS)
Gray literature: An important resource in systematic reviews.
Paez, Arsenio
2017-08-01
Systematic reviews aide the analysis and dissemination of evidence, using rigorous and transparent methods to generate empirically attained answers to focused research questions. Identifying all evidence relevant to the research questions is an essential component, and challenge, of systematic reviews. Gray literature, or evidence not published in commercial publications, can make important contributions to a systematic review. Gray literature can include academic papers, including theses and dissertations, research and committee reports, government reports, conference papers, and ongoing research, among others. It may provide data not found within commercially published literature, providing an important forum for disseminating studies with null or negative results that might not otherwise be disseminated. Gray literature may thusly reduce publication bias, increase reviews' comprehensiveness and timeliness, and foster a balanced picture of available evidence. Gray literature's diverse formats and audiences can present a significant challenge in a systematic search for evidence. However, the benefits of including gray literature may far outweigh the cost in time and resource needed to search for it, and it is important for it to be included in a systematic review or review of evidence. A carefully thought out gray literature search strategy may be an invaluable component of a systematic review. This narrative review provides guidance about the benefits of including gray literature in a systematic review, and sources for searching through gray literature. An illustrative example of a search for evidence within gray literature sources is presented to highlight the potential contributions of such a search to a systematic review. Benefits and challenges of gray literature search methods are discussed, and recommendations made. © 2017 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.
Grey literature: An important resource in systematic reviews.
Paez, Arsenio
2017-12-21
Systematic reviews aid the analysis and dissemination of evidence, using rigorous and transparent methods to generate empirically attained answers to focused research questions. Identifying all evidence relevant to the research questions is an essential component, and challenge, of systematic reviews. Grey literature, or evidence not published in commercial publications, can make important contributions to a systematic review. Grey literature can include academic papers, including theses and dissertations, research and committee reports, government reports, conference papers, and ongoing research, among others. It may provide data not found within commercially published literature, providing an important forum for disseminating studies with null or negative results that might not otherwise be disseminated. Grey literature may thusly reduce publication bias, increase reviews' comprehensiveness and timeliness and foster a balanced picture of available evidence. Grey literature's diverse formats and audiences can present a significant challenge in a systematic search for evidence. However, the benefits of including grey literature may far outweigh the cost in time and resource needed to search for it, and it is important for it to be included in a systematic review or review of evidence. A carefully thought out grey literature search strategy may be an invaluable component of a systematic review. This narrative review provides guidance about the benefits of including grey literature in a systematic review, and sources for searching through grey literature. An illustrative example of a search for evidence within grey literature sources is presented to highlight the potential contributions of such a search to a systematic review. Benefits and challenges of grey literature search methods are discussed, and recommendations made. © 2017 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.
International Nuclear Information System (INIS)
Lee, S.K.; Vilela, P.; Willinsky, R.; TerBrugge, K.G.
2002-01-01
Spontaneous regression of cerebral arteriovenous malformation (AVM) is rare and poorly understood. We reviewed the clinical and angiographic findings in patients who had spontaneous regression of cerebral AVMs to determine whether common features were present. The clinical and angiographic findings of four cases from our series and 29 cases from the literature were retrospectively reviewed. The clinical and angiographic features analyzed were: age at diagnosis, initial presentation, venous drainage pattern, number of draining veins, location of the AVM, number of arterial feeders, clinical events during the interval period to thrombosis, and interval period to spontaneous thrombosis. Common clinical and angiographic features of spontaneous regression of cerebral AVMs are: intracranial hemorrhage as an initial presentation, small AVMs, and a single draining vein. Spontaneous regression of cerebral AVMs can not be predicted by clinical or angiographic features, therefore it should not be considered as an option in cerebral AVM management, despite its proven occurrence. (orig.)
Exploratory regression analysis: a tool for selecting models and determining predictor importance.
Braun, Michael T; Oswald, Frederick L
2011-06-01
Linear regression analysis is one of the most important tools in a researcher's toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at http://dl.dropbox.com/u/2480715/ERA.xlsm?dl=1) . The program investigates all 2(p) - 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits.
The importance of civilian nursing organizations: integrative literature review.
Santos, James Farley Estevam Dos; Santos, Regina Maria Dos; Costa, Laís de Miranda Crispim; Almeida, Lenira Maria Wanderley Santos de; Macêdo, Amanda Cavalcante de; Santos, Tânia Cristina Franco
2016-06-01
to identify and analyze evidence from studies about the importance of civilian nursing organizations. an integrative literature review, for which searches were conducted in the databases LILACS, PubMed/MEDLINE, SciELO, BDENF, and Scopus. sixteen articles published between the years 2004-2013 were selected, 68.75% of which were sourced from Brazilian journals and 31.25% from American journals. civilian nursing organizations are important and necessary, because they have collaborated decisively in nursing struggles in favor of the working class and society in general, and these contributions influence different axes of professional performance.
The importance of species name synonyms in literature searches
Guala, Gerald
2016-01-01
The synonyms of biological species names are shown to be an important component in comprehensive searches of electronic scientific literature databases but they are not well leveraged within the major literature databases examined. For accepted or valid species names in the Integrated Taxonomic Information System (ITIS) which have synonyms in the system, and which are found in citations within PLoS, PMC, PubMed or Scopus, both the percentage of species for which citations will not be found if synonyms are not used, and the percentage increase in number of citations found by including synonyms are very often substantial. However, there is no correlation between the number of synonyms per species and the magnitude of the effect. Further, the number of citations found does not generally increase proportionally to the number of synonyms available. Users looking for literature on specific species across all of the resources investigated here are often missing large numbers of citations if they are not manually augmenting their searches with synonyms. Of course, missing citations can have serious consequences by effectively hiding critical information. Literature searches should include synonym relationships and a new web service in ITIS, with examples of how to apply it to this issue, was developed as a result of this study, and is here announced, to aide in this.
The Importance of Species Name Synonyms in Literature Searches.
Guala, Gerald F
2016-01-01
The synonyms of biological species names are shown to be an important component in comprehensive searches of electronic scientific literature databases but they are not well leveraged within the major literature databases examined. For accepted or valid species names in the Integrated Taxonomic Information System (ITIS) which have synonyms in the system, and which are found in citations within PLoS, PMC, PubMed or Scopus, both the percentage of species for which citations will not be found if synonyms are not used, and the percentage increase in number of citations found by including synonyms are very often substantial. However, there is no correlation between the number of synonyms per species and the magnitude of the effect. Further, the number of citations found does not generally increase proportionally to the number of synonyms available. Users looking for literature on specific species across all of the resources investigated here are often missing large numbers of citations if they are not manually augmenting their searches with synonyms. Of course, missing citations can have serious consequences by effectively hiding critical information. Literature searches should include synonym relationships and a new web service in ITIS, with examples of how to apply it to this issue, was developed as a result of this study, and is here announced, to aide in this.
The Importance of Species Name Synonyms in Literature Searches.
Directory of Open Access Journals (Sweden)
Gerald F Guala
Full Text Available The synonyms of biological species names are shown to be an important component in comprehensive searches of electronic scientific literature databases but they are not well leveraged within the major literature databases examined. For accepted or valid species names in the Integrated Taxonomic Information System (ITIS which have synonyms in the system, and which are found in citations within PLoS, PMC, PubMed or Scopus, both the percentage of species for which citations will not be found if synonyms are not used, and the percentage increase in number of citations found by including synonyms are very often substantial. However, there is no correlation between the number of synonyms per species and the magnitude of the effect. Further, the number of citations found does not generally increase proportionally to the number of synonyms available. Users looking for literature on specific species across all of the resources investigated here are often missing large numbers of citations if they are not manually augmenting their searches with synonyms. Of course, missing citations can have serious consequences by effectively hiding critical information. Literature searches should include synonym relationships and a new web service in ITIS, with examples of how to apply it to this issue, was developed as a result of this study, and is here announced, to aide in this.
Imported brucellosis: A case series and literature review.
Norman, Francesca F; Monge-Maillo, Begoña; Chamorro-Tojeiro, Sandra; Pérez-Molina, Jose-Antonio; López-Vélez, Rogelio
2016-01-01
Brucellosis is one of the main neglected zoonotic diseases. Several factors may contribute to the epidemiology of brucellosis. Imported cases, mainly in travellers but also in recently arrived immigrants, and cases associated with imported products, appear to be infrequently reported. Cases of brucellosis diagnosed at a referral unit for imported diseases in Europe were described and a review of the literature on imported cases and cases associated with contaminated imported products was performed. Most imported cases were associated with traditional risk factors such as travel/consumption of unpasteurized dairy products in endemic countries. Cases associated with importation of food products or infected animals also occurred. Although a lower disease incidence of brucellosis has been reported in developed countries, a higher incidence may still occur in specific populations, as illustrated by cases in Hispanic patients in the USA and in Turkish immigrants in Germany. Imported brucellosis appears to present with similar protean manifestations and both classical and infrequent modes of acquisition are described, leading on occasions to mis-diagnoses and diagnostic delays. Importation of Brucella spp. especially into non-endemic areas, or areas which have achieved recent control of both animal and human brucellosis, may have public health repercussions and timely recognition is essential. Copyright © 2016 Elsevier Ltd. All rights reserved.
The importance of the chosen technique to estimate diffuse solar radiation by means of regression
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Arslan, Talha; Altyn Yavuz, Arzu [Department of Statistics. Science and Literature Faculty. Eskisehir Osmangazi University (Turkey)], email: mtarslan@ogu.edu.tr, email: aaltin@ogu.edu.tr; Acikkalp, Emin [Department of Mechanical and Manufacturing Engineering. Engineering Faculty. Bilecik University (Turkey)], email: acikkalp@gmail.com
2011-07-01
The Ordinary Least Squares (OLS) method is one of the most frequently used for estimation of diffuse solar radiation. The data set must provide certain assumptions for the OLS method to work. The most important is that the regression equation offered by OLS error terms must fit within the normal distribution. Utilizing an alternative robust estimator to get parameter estimations is highly effective in solving problems where there is a lack of normal distribution due to the presence of outliers or some other factor. The purpose of this study is to investigate the value of the chosen technique for the estimation of diffuse radiation. This study described alternative robust methods frequently used in applications and compared them with the OLS method. Making a comparison of the data set analysis of the OLS and that of the M Regression (Huber, Andrews and Tukey) techniques, it was study found that robust regression techniques are preferable to OLS because of the smoother explanation values.
Directory of Open Access Journals (Sweden)
C. Wu
2018-03-01
Full Text Available Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS, Deming regression (DR, orthogonal distance regression (ODR, weighted ODR (WODR, and York regression (YR. We first introduce a new data generation scheme that employs the Mersenne twister (MT pseudorandom number generator. The numerical simulations are also improved by (a refining the parameterization of nonlinear measurement uncertainties, (b inclusion of a linear measurement uncertainty, and (c inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot was developed to facilitate the implementation of error-in-variables regressions.
Wu, Cheng; Zhen Yu, Jian
2018-03-01
Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS), Deming regression (DR), orthogonal distance regression (ODR), weighted ODR (WODR), and York regression (YR). We first introduce a new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator. The numerical simulations are also improved by (a) refining the parameterization of nonlinear measurement uncertainties, (b) inclusion of a linear measurement uncertainty, and (c) inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot) was developed to facilitate the implementation of error-in-variables regressions.
Forecasting on the total volumes of Malaysia's imports and exports by multiple linear regression
Beh, W. L.; Yong, M. K. Au
2017-04-01
This study is to give an insight on the doubt of the important of macroeconomic variables that affecting the total volumes of Malaysia's imports and exports by using multiple linear regression (MLR) analysis. The time frame for this study will be determined by using quarterly data of the total volumes of Malaysia's imports and exports covering the period between 2000-2015. The macroeconomic variables will be limited to eleven variables which are the exchange rate of US Dollar with Malaysia Ringgit (USD-MYR), exchange rate of China Yuan with Malaysia Ringgit (RMB-MYR), exchange rate of European Euro with Malaysia Ringgit (EUR-MYR), exchange rate of Singapore Dollar with Malaysia Ringgit (SGD-MYR), crude oil prices, gold prices, producer price index (PPI), interest rate, consumer price index (CPI), industrial production index (IPI) and gross domestic product (GDP). This study has applied the Johansen Co-integration test to investigate the relationship among the total volumes to Malaysia's imports and exports. The result shows that crude oil prices, RMB-MYR, EUR-MYR and IPI play important roles in the total volumes of Malaysia's imports. Meanwhile crude oil price, USD-MYR and GDP play important roles in the total volumes of Malaysia's exports.
Italian Literature in Russia. Some Recent Important Contributions
Directory of Open Access Journals (Sweden)
Claudia Lasorsa Siedina
2013-02-01
Full Text Available The paper presents an updated review of translations of contemporary Italian literature published in two issues of the popular Russian literary magazine “Inostrannaja literatura”. The first issue (2008, 10, entitled Italian Literature in Search of a Form, published translations by poets, prose writers, especially authors of stories, specimens of classic authors of the twentieth century: futurists, expressionists, surrealists, and writers of critical essays. Translation of some precious octaves of Ariosto’s poem Orlando furioso were also included. The second ‘special’ issue (2011, 8, entitled Italy: Seasons, is devoted to contemporary women writers of fiction. Particularly valuable is the Literature heritage section, which contains translations of Vivaldi’s sonnets by M. Amelin and of G.G. Belli’s Roman sonnets by E. Solonovič, both representatives of the excellence of the Russian school of translation. The two issues include a complete index of Italian authors translated into Russian between 1994 and 2011.
Buis, Dennis R.; van den Berg, René; Lycklama, Geert; van der Worp, H. Bart; Dirven, Clemens M. F.; Vandertop, W. Peter
2004-01-01
OBJECTIVE AND IMPORTANCE: Complete spontaneous obliteration of a brain arteriovenous malformation (AVM) is a rare event, with 67 angiographically proven cases in the world literature. We present a new case and a systematic literature review to determine possible mechanisms underlying this unusual
Ahmadi Moghaddam, Parnian; Cornejo, Kristine M; Hutchinson, Lloyd; Tomaszewicz, Keith; Dresser, Karen; Deng, April; OʼDonnell, Patrick
2016-11-01
Merkel cell carcinoma (MCC) is a rare primary cutaneous neuroendocrine tumor that typically occurs on the head and neck of the elderly and follows an aggressive clinical course. Merkel cell polyomavirus (MCPyV) has been identified in up to 80% of cases and has been shown to participate in MCC tumorigenesis. Complete spontaneous regression of MCC has been rarely reported in the literature. We describe a case of a 79-year-old man that presented with a rapidly growing, 3-cm mass on the left jaw. An incisional biopsy revealed MCC. Additional health issues were discovered in the preoperative workup of this patient which delayed treatment. One month after the biopsy, the lesion showed clinical regression in the absence of treatment. Wide excision of the biopsy site with sentinel lymph node dissection revealed no evidence of MCC 2 months later. The tumor cells in the patient's biopsy specimen were negative for MCPyV by polymerase chain reaction and immunohistochemistry (CM2B4 antibody, Santa Cruz, CA). The exact mechanism for complete spontaneous regression in MCC is unknown. To our knowledge, only 2 previous studies evaluated the presence of MCPyV by polymerase chain reaction in MCC with spontaneous regression. Whether the presence or absence of MCPyV correlates with spontaneous regression warrants further investigation.
Energy Technology Data Exchange (ETDEWEB)
Nimbalkar, Sachin U. [ORNL; Wenning, Thomas J. [ORNL; Guo, Wei [ORNL
2017-08-01
In the United States, manufacturing facilities account for about 32% of total domestic energy consumption in 2014. Robust energy tracking methodologies are critical to understanding energy performance in manufacturing facilities. Due to its simplicity and intuitiveness, the classic energy intensity method (i.e. the ratio of total energy use over total production) is the most widely adopted. However, the classic energy intensity method does not take into account the variation of other relevant parameters (i.e. product type, feed stock type, weather, etc.). Furthermore, the energy intensity method assumes that the facilities’ base energy consumption (energy use at zero production) is zero, which rarely holds true. Therefore, it is commonly recommended to utilize regression models rather than the energy intensity approach for tracking improvements at the facility level. Unfortunately, many energy managers have difficulties understanding why regression models are statistically better than utilizing the classic energy intensity method. While anecdotes and qualitative information may convince some, many have major reservations about the accuracy of regression models and whether it is worth the time and effort to gather data and build quality regression models. This paper will explain why regression models are theoretically and quantitatively more accurate for tracking energy performance improvements. Based on the analysis of data from 114 manufacturing plants over 12 years, this paper will present quantitative results on the importance of utilizing regression models over the energy intensity methodology. This paper will also document scenarios where regression models do not have significant relevance over the energy intensity method.
Lorenzo-Seva, Urbano; Ferrando, Pere J
2011-03-01
We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.
PageRank as a method to rank biomedical literature by importance.
Yates, Elliot J; Dixon, Louise C
2015-01-01
Optimal ranking of literature importance is vital in overcoming article overload. Existing ranking methods are typically based on raw citation counts, giving a sum of 'inbound' links with no consideration of citation importance. PageRank, an algorithm originally developed for ranking webpages at the search engine, Google, could potentially be adapted to bibliometrics to quantify the relative importance weightings of a citation network. This article seeks to validate such an approach on the freely available, PubMed Central open access subset (PMC-OAS) of biomedical literature. On-demand cloud computing infrastructure was used to extract a citation network from over 600,000 full-text PMC-OAS articles. PageRanks and citation counts were calculated for each node in this network. PageRank is highly correlated with citation count (R = 0.905, P PageRank can be trivially computed on commodity cluster hardware and is linearly correlated with citation count. Given its putative benefits in quantifying relative importance, we suggest it may enrich the citation network, thereby overcoming the existing inadequacy of citation counts alone. We thus suggest PageRank as a feasible supplement to, or replacement of, existing bibliometric ranking methods.
Landi, Alessandro; Marotta, Nicola; Morselli, Carlotta; Marongiu, Alessandra; Delfini, Roberto
2013-02-01
Several techniques have been proposed for treating cervical spine instability due to rheumatoid arthritis. The aim of this study was to screen the different treatment options used in this pathology to evaluate the best form of treatment when the progression of rheumatoid disease affected the cranio-vertebral junction (CVJ) stability. The most important purpose of this study was to achieve both the efficacy of occipito-cervical fusion (OCF) to stabilize the occipitocervical junction and stop pannus progression. The authors describe their case example and stress, in the light of a literature review, the hypothesis that a stable biomechanical system extended to all the spaces involved, has both direct and indirect effects on RA pannus progression and the condition responsible for its formation, such as inflammation and articular hypermobility. Hence, the aim of this study is to advance this thesis, which may be extended to a wider statistical sample, with the same characteristics. A systematic literature research of case report articles, review articles, original articles, and prospective cohort studies, published from 1978 to 2011, was performed using PUBMED to analyze the different surgical strategies of RA involving CVJ and the role of OCF in these conditions. The key words used for the search the were: "inflammatory cervical pannus regression", "rheumatoid arthritis of the cranio-cervical junction", "occipito-cervical fusion", "treatment option in rheumatoid cervical instability", "altanto-axial dislocation", "craniovertebral junction" and "surgical technique". In addition, the authors reported their experience in a patient affected by erosive rheumatoid arthritis (ERA) with an anterior and posterior pannus involving C0-C1-C2. They decided to report this exemplative case to emphasize their own assumptions concerning the association between a posterior bony fusion, the arrest of anterior pannus progression and the improvement of functional outcome, without, however
Directory of Open Access Journals (Sweden)
Karla M.N. Ribeiro
2002-09-01
Full Text Available Algumas doenças neurológicas podem apresentar sinais e sintomas psiquiátricos, portanto a exploração do diagnóstico etiológico é crucial. O objetivo deste estudo é relatar o caso de um paciente com um distúrbio neurológico, diagnosticado durante internação psiquiátrica. Um menino com desenvolvimento neuropsicomotor normal até 3 anos, quando começou a apresentar crises epilépticas, seguidas por distúrbio de comportamento e deterioração da linguagem. Durante o acompanhamento neurológico, o paciente foi encaminhado ao Departamento de Psiquiatria com a suspeita de autismo, regressão autística (RA. Durante internação, o diagnóstico de síndrome de Landau-Kleffner (SLK foi estabelecido em bases clínicas e eletrencefalográficas. A SLK é caracterizada por afasia adquirida, epilepsia, anormalidades eletrencefalográficas e distúrbios de comportamento, incluindo traços autísticos. A regressão da linguagem é observada na SLK e na RA. Enfatizamos as principais diferenças entre estas entidades, pois o diagnóstico errôneo adia a intervenção precoce e benefícios, como observado em nosso caso.Some neurological disorders may present psychiatric signs and symptoms, therefore the search for an etiological diagnosis is crucial. The aim of this study is to report the case of a patient with a neurological disorder, diagnosed during a psychiatric admission. A boy with normal neuropsychomotor development until the age of 3 years, started presenting epileptic seizures, followed by behavioral disorder and language deterioration. During neurologic follow-up, the patient was referred to the Psychiatry Department with a diagnosis of autism, in this case an autistic regression (AR. During his admission, diagnosis of Landau-Kleffner syndrome (LKS was established on clinical and EEG grounds. LKS is characterized by acquired aphasia, epilepsy, EEG abnormalities and behavioral changes, including autistic traits. Language regression is
Clinical importance of the middle meningeal artery: A review of the literature
Yu, Jinlu; Guo, Yunbao; Xu, Baofeng; Xu, Kan
2016-01-01
The middle meningeal artery (MMA) is a very important artery in neurosurgery. Many diseases, including dural arteriovenous fistula (DAVF), pseudoaneurysm, true aneurysm, traumatic arteriovenous fistula (AVF), moyamoya disease (MMD), recurrent chronic subdural hematoma (CSDH), migraine and meningioma, can involve the MMA. In these diseases, the lesions occur in either the MMA itself and treatment is necessary, or the MMA is used as the pathway to treat the lesions; therefore, the MMA is very important to the development and treatment of a variety of neurosurgical diseases. However, no systematic review describing the importance of MMA has been published. In this study, we used the PUBMED database to perform a review of the literature on the MMA to increase our understanding of its role in neurosurgery. After performing this review, we found that the MMA was commonly used to access DAVFs and meningiomas. Pseudoaneurysms and true aneurysms in the MMA can be effectively treated via endovascular or surgical removal. In MMD, the MMA plays a very important role in the development of collateral circulation and indirect revascularization. For recurrent CDSHs, after burr hole irrigation and drainage have failed, MMA embolization may be attempted. The MMA can also contribute to the occurrence and treatment of migraines. Because the ophthalmic artery can ectopically originate from the MMA, caution must be taken to avoid causing damage to the MMA during operations. PMID:27766029
Clinical importance of the middle meningeal artery: A review of the literature.
Yu, Jinlu; Guo, Yunbao; Xu, Baofeng; Xu, Kan
2016-01-01
The middle meningeal artery (MMA) is a very important artery in neurosurgery. Many diseases, including dural arteriovenous fistula (DAVF), pseudoaneurysm, true aneurysm, traumatic arteriovenous fistula (AVF), moyamoya disease (MMD), recurrent chronic subdural hematoma (CSDH), migraine and meningioma, can involve the MMA. In these diseases, the lesions occur in either the MMA itself and treatment is necessary, or the MMA is used as the pathway to treat the lesions; therefore, the MMA is very important to the development and treatment of a variety of neurosurgical diseases. However, no systematic review describing the importance of MMA has been published. In this study, we used the PUBMED database to perform a review of the literature on the MMA to increase our understanding of its role in neurosurgery. After performing this review, we found that the MMA was commonly used to access DAVFs and meningiomas. Pseudoaneurysms and true aneurysms in the MMA can be effectively treated via endovascular or surgical removal. In MMD, the MMA plays a very important role in the development of collateral circulation and indirect revascularization. For recurrent CDSHs, after burr hole irrigation and drainage have failed, MMA embolization may be attempted. The MMA can also contribute to the occurrence and treatment of migraines. Because the ophthalmic artery can ectopically originate from the MMA, caution must be taken to avoid causing damage to the MMA during operations.
The Importance of Magnesium in the Human Body: A Systematic Literature Review.
Glasdam, Sidsel-Marie; Glasdam, Stinne; Peters, Günther H
2016-01-01
Magnesium, the second and fourth most abundant cation in the intracellular compartment and whole body, respectively, is of great physiologic importance. Magnesium exists as bound and free ionized forms depending on temperature, pH, ionic strength, and competing ions. Free magnesium participates in many biochemical processes and is most commonly measured by ion-selective electrode. This analytical approach is problematic because complete selectivity is not possible due to competition with other ions, i.e., calcium, and pH interference. Unfortunately, many studies have focused on measurement of total magnesium rather than its free bioactive form making it difficult to correlate to disease states. This systematic literature review presents current analytical challenges in obtaining accurate and reproducible test results for magnesium. © 2016 Elsevier Inc. All rights reserved.
Elands, Rachel J J; Simons, Colinda C J M; Dongen, Martien van; Schouten, Leo J; Verhage, Bas A J; van den Brandt, Piet A; Weijenberg, Matty P
2016-01-01
In animal models, long-term moderate energy restriction (ER) is reported to decelerate carcinogenesis, whereas the effect of severe ER is inconsistent. The impact of early-life ER on cancer risk has never been reviewed systematically and quantitatively based on observational studies in humans. We conducted a systematic review of observational studies and a meta-(regression) analysis on cohort studies to clarify the association between early-life ER and organ site-specific cancer risk. PubMed and EMBASE (1982 -August 2015) were searched for observational studies. Summary relative risks (RRs) were estimated using a random effects model when available ≥3 studies. Twenty-four studies were included. Eleven publications, emanating from seven prospective cohort studies and some reporting on multiple cancer endpoints, met the inclusion criteria for quantitative analysis. Women exposed to early-life ER (ranging from 220-1660 kcal/day) had a higher breast cancer risk than those not exposed (RRRE all ages = 1.28, 95% CI: 1.05-1.56; RRRE for 10-20 years of age = 1.21, 95% CI: 1.09-1.34). Men exposed to early-life ER (ranging from 220-800kcal/day) had a higher prostate cancer risk than those not exposed (RRRE = 1.16, 95% CI: 1.03-1.30). Summary relative risks were not computed for colorectal cancer, because of heterogeneity, and for stomach-, pancreas-, ovarian-, and respiratory cancer because there were <3 available studies. Longer duration of exposure to ER, after adjustment for severity, was positively associated with overall cancer risk in women (p = 0.02). Ecological studies suggest that less severe ER is generally associated with a reduced risk of cancer. Early-life transient severe ER seems to be associated with increased cancer risk in the breast (particularly ER exposure at adolescent age) and prostate. The duration, rather than severity of exposure to ER, seems to positively influence relative risk estimates. This result should be interpreted with caution due to the
Clinical importance of the anterior choroidal artery: a review of the literature.
Yu, Jing; Xu, Ning; Zhao, Ying; Yu, Jinlu
2018-01-01
The anterior choroidal artery (AChA) is a critical artery in brain physiology and function. The AChA is involved in many diseases, including aneurysm, brain infarct, Moyamoya disease (MMD), brain tumor, arteriovenous malformation (AVM), etc. The AChA is vulnerable to damage during the treatment of these diseases and is thus a very important vessel. However, a comprehensive systematic review of the importance of the AChA is currently lacking. In this study, we used the PUBMED database to perform a literature review of the AChA to increase our understanding of its role in neurophysiology. Although the AChA is a small thin artery, it supplies an extremely important region of the brain. The AChA consists of cisternal and plexal segments, and the point of entry into the choroidal plexus is known as the plexal point. During treatment for aneurysms, tumors, AVM or AVF, the AChA cisternal segments should be preserved as a pathway to prevent the infarction of the AChA target region in the brain. In MMD, a dilated AChA provides collateral flow for posterior circulation. In brain infarcts, rapid treatment is necessary to prevent brain damage. In Parkinson disease (PD), the role of the AChA is unclear. In trauma, the AChA can tear and result in intracranial hematoma. In addition, both chronic and non-chronic branch vessel occlusions in the AChA are clinically silent and should not deter aneurysm treatment with flow diversion. Based on the data available, the AChA is a highly essential vessel.
Mariani, L; Minora, T; Ventresca, G P
1996-12-01
The authors highlight the essential role of pharmacovigilance and the need for a simple, efficient and low-cost system of adverse reaction (AR) reporting which could cover the whole population and all marketed drugs, and suggest that the only one presently viable is based on spontaneous reporting. To support their proposal the authors provide a definition of AR and of the different monitoring system, and list as many drugs as possible to find in the literature that have been associated with a specific AR, together with the active molecule, the therapeutic indication, the features of the AR and the regulatory actions (withdrawal from the market, restriction of use). Moreover, by describing the "history" behind some of these drugs the authors highlight the contribution that pharmacovigilance and spontaneous reporting have had to the development of regulations for approval and marketing of new drugs. It is also highlighted how some of these unexpected events (thalidomide, DES) have had a significant and important contribution to pharmacological and toxicological knowledge.
International Nuclear Information System (INIS)
Menten, Fabio; Cheze, Benoit; Patouillard, Laure; Bouvart, Frederique
2013-01-01
This article presents the results of a literature review performs with a meta-regression analysis (MRA) that focuses on the estimates of advanced biofuel Greenhouse Gas (GHG) emissions assessed with a Life Cycle Assessment (LCA) approach. The mean GHG emissions of both second (G2) and third generation (G3) biofuels and the effects of factors influencing these estimates are identified and quantified by means of specific statistical methods. 47 LCA studies are included in the database, providing 593 estimates. Each study estimate of the database is characterized by i) technical data/characteristics, ii) author's methodological choices and iii) typology of the study under consideration. The database is composed of both the vector of these estimates - expressed in grams of CO 2 equivalent per MJ of biofuel (g CO 2 eq/MJ) - and a matrix containing vectors of predictor variables which can be continuous or dummy variables. The former is the dependent variable while the latter corresponds to the explanatory variables of the meta-regression model. Parameters are estimated by mean of econometrics methods. Our results clearly highlight a hierarchy between G3 and G2 biofuels: life cycle GHG emissions of G3 biofuels are statistically higher than those of Ethanol which, in turn, are superior to those of BtL. Moreover, this article finds empirical support for many of the hypotheses formulated in narrative literature surveys concerning potential factors which may explain estimates variations. Finally, the MRA results are used to address the harmonization issue in the field of advanced biofuels GHG emissions thanks to the technique of benefits transfer using meta-regression models. The range of values hence obtained appears to be lower than the fossil fuel reference (about 83.8 in g CO 2 eq/ MJ). However, only Ethanol and BtL do comply with the GHG emission reduction thresholds for biofuels defined in both the American and European directives. (authors)
International Nuclear Information System (INIS)
Tai, Bee-Choo; Grundy, Richard; Machin, David
2011-01-01
Purpose: To accurately model the cumulative need for radiotherapy in trials designed to delay or avoid irradiation among children with malignant brain tumor, it is crucial to account for competing events and evaluate how each contributes to the timing of irradiation. An appropriate choice of statistical model is also important for adequate determination of sample size. Methods and Materials: We describe the statistical modeling of competing events (A, radiotherapy after progression; B, no radiotherapy after progression; and C, elective radiotherapy) using proportional cause-specific and subdistribution hazard functions. The procedures of sample size estimation based on each method are outlined. These are illustrated by use of data comparing children with ependymoma and other malignant brain tumors. The results from these two approaches are compared. Results: The cause-specific hazard analysis showed a reduction in hazards among infants with ependymoma for all event types, including Event A (adjusted cause-specific hazard ratio, 0.76; 95% confidence interval, 0.45-1.28). Conversely, the subdistribution hazard analysis suggested an increase in hazard for Event A (adjusted subdistribution hazard ratio, 1.35; 95% confidence interval, 0.80-2.30), but the reduction in hazards for Events B and C remained. Analysis based on subdistribution hazard requires a larger sample size than the cause-specific hazard approach. Conclusions: Notable differences in effect estimates and anticipated sample size were observed between methods when the main event showed a beneficial effect whereas the competing events showed an adverse effect on the cumulative incidence. The subdistribution hazard is the most appropriate for modeling treatment when its effects on both the main and competing events are of interest.
Modi, Hitesh N; Suh, Seung-Woo; Yang, Jae-Hyuk; Hong, Jae-Young; Venkatesh, Kp; Muzaffar, Nasir
2010-11-04
Child with mild scoliosis is always a subject of interest for most orthopaedic surgeons regarding progression. Literature described Hueter-Volkmann theory regarding disc and vertebral wedging, and muscular imbalance for the progression of adolescent idiopathic scoliosis. However, many authors reported spontaneous resolution of curves also without any reason for that and the rate of resolution reported is almost 25%. Purpose of this study was to question the role of paraspinal muscle tuning/balancing mechanism, especially in patients with idiopathic scoliosis with early mild curve, for spontaneous regression or progression as well as changing pattern of curves. An observational study of serial radiograms in 169 idiopathic scoliosis children (with minimum follow-up one year) was carried. All children with Cobb angle change and progression of their curves, respectively. Additionally changes in the pattern of curve were also noted. Average age was 9.2 years at first visit and 10.11 years at final follow-up with an average follow-up of 21 months. 32.5% (55/169), 41.4% (70/169) and 26% (44/169) children exhibited regression, no change and progression in their curves, respectively. 46.1% of children (78/169) showed changing pattern of their curves during the follow-up visits before it settled down to final curve. Comparing final fate of curve with side of curve and number of curves it did not show any relationship (p > 0.05) in our study population. Possible reason for changing patterns could be better explained by the tuning/balancing mechanism of spinal column that makes an effort to balance the spine and result into spontaneous regression or prevent further progression of curve. If this which we called as "tuning/balancing mechanism" fails, curve will ultimately progress.
Directory of Open Access Journals (Sweden)
Modi Hitesh N
2010-11-01
Full Text Available Abstract Background Child with mild scoliosis is always a subject of interest for most orthopaedic surgeons regarding progression. Literature described Hueter-Volkmann theory regarding disc and vertebral wedging, and muscular imbalance for the progression of adolescent idiopathic scoliosis. However, many authors reported spontaneous resolution of curves also without any reason for that and the rate of resolution reported is almost 25%. Purpose of this study was to question the role of paraspinal muscle tuning/balancing mechanism, especially in patients with idiopathic scoliosis with early mild curve, for spontaneous regression or progression as well as changing pattern of curves. Methods An observational study of serial radiograms in 169 idiopathic scoliosis children (with minimum follow-up one year was carried. All children with Cobb angle Results Average age was 9.2 years at first visit and 10.11 years at final follow-up with an average follow-up of 21 months. 32.5% (55/169, 41.4% (70/169 and 26% (44/169 children exhibited regression, no change and progression in their curves, respectively. 46.1% of children (78/169 showed changing pattern of their curves during the follow-up visits before it settled down to final curve. Comparing final fate of curve with side of curve and number of curves it did not show any relationship (p > 0.05 in our study population. Conclusion Possible reason for changing patterns could be better explained by the tuning/balancing mechanism of spinal column that makes an effort to balance the spine and result into spontaneous regression or prevent further progression of curve. If this which we called as "tuning/balancing mechanism" fails, curve will ultimately progress.
Directory of Open Access Journals (Sweden)
Lisa-Britt Fischer
2016-05-01
Full Text Available This article explores the role of actors and agency in the literature on sustainability transitions. We reviewed 386 journal articles on transition management and sustainability transitions listed in Scopus from 1995 to 2014. We investigate the thesis that actors have been neglected in this literature in favor of more abstract system concepts. Results show that this thesis cannot be confirmed on a general level. Rather, we find a variety of different approaches, depending on the systemic level, for clustering actors and agency as niche, regime, and landscape actors; the societal realm; different levels of governance; and intermediaries. We also differentiate between supporting and opposing actors. We find that actor roles in transitions are erratic, since their roles can change over the course of time, and that actors can belong to different categories. We conclude by providing recommendations for a comprehensive typology of actors in sustainability transitions.
Ozge, C; Toros, F; Bayramkaya, E; Camdeviren, H; Sasmaz, T
2006-08-01
The purpose of this study is to evaluate the most important sociodemographic factors on smoking status of high school students using a broad randomised epidemiological survey. Using in-class, self administered questionnaire about their sociodemographic variables and smoking behaviour, a representative sample of total 3304 students of preparatory, 9th, 10th, and 11th grades, from 22 randomly selected schools of Mersin, were evaluated and discriminative factors have been determined using appropriate statistics. In addition to binary logistic regression analysis, the study evaluated combined effects of these factors using classification and regression tree methodology, as a new statistical method. The data showed that 38% of the students reported lifetime smoking and 16.9% of them reported current smoking with a male predominancy and increasing prevalence by age. Second hand smoking was reported at a 74.3% frequency with father predominance (56.6%). The significantly important factors that affect current smoking in these age groups were increased by household size, late birth rank, certain school types, low academic performance, increased second hand smoking, and stress (especially reported as separation from a close friend or because of violence at home). Classification and regression tree methodology showed the importance of some neglected sociodemographic factors with a good classification capacity. It was concluded that, as closely related with sociocultural factors, smoking was a common problem in this young population, generating important academic and social burden in youth life and with increasing data about this behaviour and using new statistical methods, effective coping strategies could be composed.
Gibrat’s law and quantile regressions
DEFF Research Database (Denmark)
Distante, Roberta; Petrella, Ivan; Santoro, Emiliano
2017-01-01
The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important...
Directory of Open Access Journals (Sweden)
Stefan J. Teipel
2015-01-01
Penalized regression yielded more parsimonious models than unpenalized stepwise regression for the integration of multiregional and multimodal imaging information. The advantage of penalized regression was particularly strong with a high number of collinear predictors.
Martin, Amber L; Marvel, Jessica; Fahrbach, Kyle; Cadarette, Sarah M; Wilcox, Teresa K; Donohue, James F
2016-04-16
This study investigated the relationship between changes in lung function (as measured by forced expiratory volume in one second [FEV1]) and the St. George's Respiratory Questionnaire (SGRQ) and economically significant outcomes of exacerbations and health resource utilization, with an aim to provide insight into whether the effects of COPD treatment on lung function and health status relate to a reduced risk for exacerbations. A systematic literature review was conducted in MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials to identify randomized controlled trials of adult COPD patients published in English since 2002 in order to relate mean change in FEV1 and SGRQ total score to exacerbations and hospitalizations. These predictor/outcome pairs were analyzed using sample-size weighted regression analyses, which estimated a regression slope relating the two treatment effects, as well as a confidence interval and a test of statistical significance. Sixty-seven trials were included in the analysis. Significant relationships were seen between: FEV1 and any exacerbation (time to first exacerbation or patients with at least one exacerbation, p = 0.001); between FEV1 and moderate-to-severe exacerbations (time to first exacerbation, patients with at least one exacerbation, or annualized rate, p = 0.045); between SGRQ score and any exacerbation (time to first exacerbation or patients with at least one exacerbation, p = 0.0002) and between SGRQ score and moderate-to-severe exacerbations (time to first exacerbation or patients with at least one exacerbation, p = 0.0279; annualized rate, p = 0.0024). Relationships between FEV1 or SGRQ score and annualized exacerbation rate for any exacerbation or hospitalized exacerbations were not significant. The regression analysis demonstrated a significant association between improvements in FEV1 and SGRQ score and lower risk for COPD exacerbations. Even in cases of non-significant relationships
Impacts of Imported Liquefied Natural Gas on Residential Appliance Components: Literature Review
Energy Technology Data Exchange (ETDEWEB)
Lekov, Alex; Sturges, Andy; Wong-Parodi, Gabrielle
2009-12-09
An increasing share of natural gas supplies distributed to residential appliances in the U.S. may come from liquefied natural gas (LNG) imports. The imported gas will be of a higher Wobbe number than domestic gas, and there is concern that it could produce more pollutant emissions at the point of use. This report will review recently undertaken studies, some of which have observed substantial effects on various appliances when operated on different mixtures of imported LNG. While we will summarize findings of major studies, we will not try to characterize broad effects of LNG, but describe how different components of the appliance itself will be affected by imported LNG. This paper considers how the operation of each major component of the gas appliances may be impacted by a switch to LNG, and how this local impact may affect overall safety, performance and pollutant emissions.
Directory of Open Access Journals (Sweden)
Abdulhekim Koçin
2017-12-01
Full Text Available The language used by a poet or an author in a literary work in verse or prose, and the way he uses idioms, proverbs and literary arts in that language indicate the success of his art. The achievement of author in this matter makes him well known in the country where he lives. However, the topic choice is as important as an artist’s language skills. That’s why poets and authors prefer universal subjects such as love, death, religion, religious personalities (like prophets, saints, etc. and humanity (man's way of living and right to live in their works. If artists produce universal subjects with a clear language, a fluent wording and a strong story line, this achievement makes them famous both nationally and internationally. This fact can also be applied to the literature of any nation. Literary works containing local subjects cannot take their place in the world’s literary history. Classical Turkish literature (Ottoman Period Turkish Literature is extremely rich in subject matter. The period in which Classical Turkish Literature continued its existence was the period when the Ottoman state dominated large geographical regions in Asia, Europe and Africa. Accordingly, the lifestyles, beliefs, traditions and customs of people from different religions, races and cultures living in these geographies; in other words, the issues that are important to these people were also reflected in the literature produced in this period. The subject of this article Virgin Mary is an important religious and a historical personality primarily for the Ottoman state’s Christians and Muslim subjects as well as those who weren’t the subjects of Ottoman Empire. In this article, first how Virgin Mary took part in the two most important sources of Classical Turkish literature, Koran and the hadiths will be summarized. Secondly, Virgin Mary’s place in Classical Turkish literature and the vocabulary and concepts that Turkish language gained through Virgin Mary will
Augé, Robert M; Toler, Heather D; Saxton, Arnold M
2016-01-01
Arbuscular mycorrhizal (AM) symbiosis often stimulates gas exchange rates of the host plant. This may relate to mycorrhizal effects on host nutrition and growth rate, or the influence may occur independently of these. Using meta-regression, we tested the strength of the relationship between AM-induced increases in gas exchange, and AM size and leaf mineral effects across the literature. With only a few exceptions, AM stimulation of carbon exchange rate (CER), stomatal conductance (g s), and transpiration rate (E) has been significantly associated with mycorrhizal stimulation of shoot dry weight, leaf phosphorus, leaf nitrogen:phosphorus ratio, and percent root colonization. The sizeable mycorrhizal stimulation of CER, by 49% over all studies, has been about twice as large as the mycorrhizal stimulation of g s and E (28 and 26%, respectively). CER has been over twice as sensitive as g s and four times as sensitive as E to mycorrhizal colonization rates. The AM-induced stimulation of CER increased by 19% with each AM-induced doubling of shoot size; the AM effect was about half as large for g s and E. The ratio of leaf N to leaf P has been more closely associated with mycorrhizal influence on leaf gas exchange than leaf P alone. The mycorrhizal influence on CER has declined markedly over the 35 years of published investigations.
Directory of Open Access Journals (Sweden)
Robert M. Augé
2016-07-01
Full Text Available Arbuscular mycorrhizal (AM symbiosis often stimulates gas exchange rates of the host plant. This may relate to mycorrhizal effects on host nutrition and growth rate, or the influence may occur independently of these. Using meta-regression, we tested the strength of the relationship between AM-induced increases in gas exchange, and AM size and leaf mineral effects across the literature. With only a few exceptions, AM stimulation of carbon exchange rate (CER, stomatal conductance (gs and transpiration rate (E has been significantly associated with mycorrhizal stimulation of shoot dry weight, leaf phosphorus, leaf nitrogen: phosphorus ratio and percent root colonization. The sizeable mycorrhizal stimulation of CER, by 49% over all studies, has been about twice as large as the mycorrhizal stimulation of gs and E (28% and 26%, respectively. Carbon exchange rate has been over twice as sensitive as gs and four times as sensitive as E to mycorrhizal colonization rates. The AM-induced stimulation of CER increased by 19% with each AM-induced doubling of shoot size; the AM effect was about half as large for gs and E. The ratio of leaf N to leaf P has been more closely associated with mycorrhizal influence on leaf gas exchange than leaf P alone. The mycorrhizal influence on CER has declined markedly over the 35 years of published investigations.
Perceived importance and responsibility for market-driven pig welfare: Literature review.
Thorslund, Cecilie A H; Aaslyng, Margit Dall; Lassen, Jesper
2017-03-01
This review explores barriers and opportunities for market-driven pig welfare in Europe. It finds, first, that consumers generally rank animal welfare as important, but they also rank it low relative to other societal problems. Second, consumers have a wide range of concerns about pig welfare, but they focus especially on naturalness. Third, pig welfare is seen as an important indicator of meat quality. Fourth, consumers tend to think that responsibility for pig welfare lies with several actors: farmers, governments and themselves. The paper concludes that there is an opportunity for the market-driven strategy to sell a narrative about naturalness supplemented with other attractive qualities (such as eating quality). It also emphasizes that pig welfare needs to be on the political/societal agenda permanently if it is to be viewed as an important issue by consumers and if consumers are to assume some sort of responsibility for it. Copyright © 2016 Elsevier Ltd. All rights reserved.
Spady, Richard; Stouli, Sami
2012-01-01
We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...
Bartel, Ricardo; Gonzalez-Compta, Xavier; Cisa, Enric; Cruellas, Francesc; Torres, Alberto; Rovira, Aleix; Manos, Manel
2017-10-20
Olfactory neuroblastoma (ONB) is a rare entity that constitutes less than 5% of nasosinusal malignancies. Mainstream treatment consists in surgical resection+/-adjuvant radiotherapy. By exposing results observed with apparition of new therapeutic options as neoadjuvant chemotherapy, the objective is to evaluate a series and a review of the current literature. A retrospective review was conducted including patients diagnosed and followed-up for ONB from 2008 to 2015 in our institution. 9 patients were included. Mean follow-up of 52.5 months (range 10-107). Kadish stage: A, 1 patient (11.1%) treated with endoscopic surgery; B, 2 patients (22.2%) treated with endoscopic surgery (one of them received adjuvant radiotherapy); C, 6 patients (66.7%), 4 patients presented intracranial extension and were treated with neoadjuvant chemotherapy followed by surgery and radiotherapy. The other 2 patients presented isolated orbital extension, treated with radical surgery (endoscopic or craniofacial resection) plus radiotherapy. The 5-year disease free and overall survival observed was 88.9%. Neoadjuvant chemotherapy could be an effective treatment for tumor reduction, improving surgical resection and reducing its complications. Copyright © 2017 Elsevier España, S.L.U. and Sociedad Española de Otorrinolaringología y Cirugía de Cabeza y Cuello. All rights reserved.
Tungiasis in Italy: An imported case of Tunga penetrans and review of the literature.
Palicelli, Andrea; Boldorini, Renzo; Campisi, Paola; Disanto, Maria Giulia; Gatti, Lucio; Portigliotti, Luca; Tosoni, Antonella; Rivasi, Francesco
2016-05-01
Tungiasis is an animal and human parasitic disease caused by fleas of the genus Tunga (Siphonaptera, Tungidae), endemic in equatorial and subtropical regions and rarely described in European countries, where clinicians and general pathologists could be not aware of this parasitic disease. To our knowledge, only 75 cases of human tungiasis (not all described in detail) were previously reported in Italy. We described a new case in a 34-year-old Italian flight attendant who developed a granuloma-like, ulcerated nodule in the subungual region of his left 5th toe, partially detaching the nail, about 20-30 days after his return from Brazil. We performed a detailed review of the literature of the Italian cases, suggesting the use of histochemical stains (especially Trichrome stain) in order to underline parasitic details. Tourism in endemic regions and globalization may result in new cases in developed countries and previously unaffected regions, therefore pathologists should consider this parasitic disease. Copyright © 2016 Elsevier GmbH. All rights reserved.
The Importance of Magnesium in the Human Body: A Systematic Literature Review
DEFF Research Database (Denmark)
Glasdam, Sidsel-Marie; Glasdam, Stinne; Peters, Günther H.J.
2016-01-01
Magnesium, the second and fourth most abundant cation in the intracellular compartment and whole body, respectively, is of great physiologic importance. Magnesium exists as bound and free ionized forms depending on temperature, pH, ionic strength, and competing ions. Free magnesium participates...
DEFF Research Database (Denmark)
Hansen, Bodil Winther; Morville, Anne-Le
Med, Cinahl, PsychInfo, and the Danish library index. Our inclusion criteria were literature that covered the value and meaning of creative activity in general and/or application of creative activities as intervention tool. Peer-reviewed articles, articles and books in English, and Scandinavian languages were......Introduction: Looking back on the history of occupational therapy, creative activities played a major part in the rehabilitation process, but have been diminished during the last decades. This review looks at the importance and application of creative activities in occupational therapy in the 21st...... century. Objectives: The aim of the review was to describe the value and importance of focusing on creative activities in occupational therapy intervention. Method: This scoping review was done as prequel to a book on creativity in occupational therapy, and based on literature search in the databases Pub...
Directory of Open Access Journals (Sweden)
DORIN COSMA
2014-05-01
Full Text Available This paper studies the influence money has on motivating the work force which takes part on both dull and creative tasks. Through a review of the most important findings on the subject, economists’ and psychologists’ theories are presented and discussed. The paper shows how, notwithstanding their high attention received from the business environment in the past, the principal-agent and the price effect theories are being replaced lately with the self determination, psychological contract and reinforcement theories in the configuration process of work incentives. Specific advantages and disadvantages of the use of financial motivators for increasing work performance are further examined and a clear example of how money and more specifically their perception affects the employees’ motivation is given. The results of the study conclude that money has an important role as a hygienic factor in the work environment but they do not represent a panacea for inducing high work motivation and are not enough for assuring the overall success, whether that we are talking about repetitive or creative activities. The late ones are exemplified by the tasks specific to the higher education sector.
The importance of perinatal autopsy. Review of the literature and series of cases.
Şorop-Florea, Maria; Ciurea, Raluca Niculina; Ioana, Mihai; Stepan, Alex Emilian; Stoica, George Alin; Tănase, Florentina; Comănescu, Maria Cristina; Novac, Marius Bogdan; Drăgan, Ioana; Pătru, Ciprian LaurenŢiu; Drăguşin, Roxana Cristina; Zorilă, George Lucian; Cărbunaru, Ovidiu Marian; Oprescu, NuŢi Daniela; Ceauşu, Iuliana; Vlădăreanu, Simona; Tudorache, Ştefania; Iliescu, Dominic Gabriel
2017-01-01
Perinatal autopsy remains the gold-standard procedure used to establish the fetal, neonatal or infant abnormalities. Progressively, perinatal pathology has become a specialized field with important roles of audit for fetal prenatal diagnostic tools, in parents counseling regarding future pregnancies, in scientific research, for epidemiology of congenital abnormalities and teaching. The differences between prenatal ultrasound and autopsy reports represent a strong argument for the autopsy examination following termination of pregnancy. The reasons for such discrepancies are related to the ultrasonographic or pathological examination conditions, the type of the anomalies, the expertise and availability of the operators. Several facts led to an undesirable increase of refusals from parents to consent to a conventional invasive autopsy: the centralization of pathology services, the poor counseling provided by non-experts in fetal medicine and the clinicians' over-appreciation of the importance of the ultrasound diagnostic investigation. Although non-invasive alternatives have been tested with promising results, conventional autopsy remains the gold standard technique for the prenatal diagnosis audit. We report and analyze several cases of prenatally diagnosed malformed fetuses with different particularities that underline the necessity of perinatal autopsy. We discuss the antenatal findings and management and post-mortem autopsies in the respective pregnancies.
[An imported dengue Fever case in Turkey and review of the literature].
Uyar, Yavuz; Aktaş, Eray; Yağcı Çağlayık, Dilek; Ergönül, Onder; Yüce, Ayşe
2013-01-01
Dengue fever is an acute viral disease that can affect all age groups in tropical and subtropical countries. The predominant vectors are the mosquitoes namely Aedes aegypti and A.albopictus. Although there have been no case reports in Turkey due to DF, there is seroepidemiological evidence indicating the presence of Dengue virus (DENV) in Turkey. In this case report we presented an imported dengue fever case. The patient was 40 years old, previously healthy male, Switzerland citizen. He had immigrated from Dubai to India two weeks ago and after one week from immigration he attended to a hospital in India because of high fever. The NS1 antigen test (Bio-Rad Laboratories, USA) was found positive and the patient was followed-up with diagnosis of dengue fever in India. During his visit to Turkey, he attended to the hospital for a routine control and his analysis revealed thrombocytopenia (PLT: 48.000/µl), leukopenia (white blood cell: 2800/µL) and elevated liver enzymes (AST: 76 U/L, ALT: 83 U/L). Fever was not detected in follow-up. The patient had petechial rash on his lower extremities. white blood cell and PLT count increased to 4100/µl and 93.000/µl, respectively. Liver function tests revealed a decrease in AST (63 U/L) and ALT (78 U/L) on the third day. The PLT count increased to 150.000/ml. Since the patient had no fever and had normal physical and laboratory findings, he was discharged from the hospital. For the confirmation of dengue fever diagnosis the serum sample was sent to National Public Health Center, Virology Reference and Research Laboratory where IgM and IgG antibodies against DENV types 1-4 were investigated by indirect immunofluorescence method (Euroimmun, Germany). The serum sample yielded positive result at the dilutions of 1/1000 for IgM and 1/10.000 for IgG. The last dilution of type 3 DENV IgM and IgG were determined high density of fluorescein, thus the serotype was identified as "DENV type 3". Travel-related diseases become important
Directory of Open Access Journals (Sweden)
Sandro César Bortoluzzi
2015-12-01
Full Text Available The research aims to map the importance and performance evaluation tools for small and medium companies. This descriptive and qualitative study analyzed 33 national articles and 21 international ones. Regarding the importance of performance evaluation for small and medium companies, the literature highlights: (i it increases the success of the network; (ii it is useful for management; (iii it strengthens competitiveness; (iv it consolidates cooperation; and, (v it increases trust among partners. Comparing the national versus international literature on the importance of performance evaluation for small and medium companies, it can be noticed similar and complementary aspects, that is, there is not disagreement between the authors. The authors use tools consolidated in the literature, such as Balanced Scorecard; Benchmarking; Performance Prism and tools proposed specifically to evaluate small and medium networks. The main dimensions evaluated are: (i exchange of information; (ii value management in networks; (Iii level of network maturity; (iv benefits of collaboration; (v social capital; (vi collective efficiency; (vii network life cycle; (viii efficiency and inefficiency of the networks; and, (ix existence and intensity of the relationship between partners. The critical analysis regarding the performance evaluation concept adopted in the present study shows that the tools proposed or implemented to evaluate small and medium business networks have gaps in the process to identify criteria, measure ordinal and cardinally, integrate and generate actions of improvement.
Directory of Open Access Journals (Sweden)
Nuriane Santos Montezano
2015-08-01
Full Text Available The article presents the reality of the new Executive Secretariat professional and its relation to the Strategic Information System. All the concepts were worked out based on the national integrative literature review. The aim was to determine what is the importance of information management and its applicability to the professional context of the Executive Secretariat. The discussion and theoretical reflection showed that the current Executive Secretary professional is prepared for the new organizational dynamics to incorporate technologically execution management information in context. This is another task that gives and confirms its multifunctional character as an important information manager figure in decision-making organizations.
Correlation and simple linear regression.
Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G
2003-06-01
In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.
Mohindra, Sandeep; Singh, Harnarayan; Savardekar, Amey
2012-01-01
To describe compound elevated fractures (CEFs) of the skull vault, with radiological pictures, management problems and prognosticative factors. The authors describe three cases of CEFs of the cranium, their mode of injury, clinical findings, radiological images and management problems. The authors have reviewed the existing literature regarding epidemiological data, neurological status, dural breech, methods of management and final outcome, in respect of CEFs. The first case had no dural breech, the second case had completely shattered dura, with extruding brain matter from the wound, while the third case had an elevated bone flap in consequence to large extradural haematoma. The patients with intact dura had relatively favourable outcome, when compared to patients with shattered dura. Three cases are added to the existing 10 such cases described in English literature. The major cause of unfavourable outcome remains sepsis and the presence of intact dura places these cases in the relatively safe category, regarding infective complications. The authors attempt at highlighting the importance of intact dura with such an injury. The review of literature supports favourable outcomes in patients having no dural breech.
Better Autologistic Regression
Directory of Open Access Journals (Sweden)
Mark A. Wolters
2017-11-01
Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.
Noussios, George; Dimitriou, Ioannis; Chatzis, Iosif; Katsourakis, Anastasios
2017-04-01
Anatomical variations of the hepatic artery are important in the planning and performance of abdominal surgical procedures. Normal hepatic anatomy occurs in approximately 80% of cases, for the remaining 20% multiple variations have been described. The purpose of this study was to review the existing literature on the hepatic anatomy and to stress out its importance in surgical practice. Two main databases were searched for eligible articles during the period 2000 - 2015, and results concerning more than 19,000 patients were included in the study. The most common variation was the replaced right hepatic artery (type III according to Michels classification) which is the chief source of blood supply to the bile duct.
Dimitriou, Ioannis; Katsourakis, Anastasios; Nikolaidou, Eirini; Noussios, George
2018-05-01
Anatomical variations or anomalies of the pancreatic ducts are important in the planning and performance of endoscopic retrograde cholangiopancreatography (ERCP) and surgical procedures of the pancreas. Normal pancreatic duct anatomy occurs in approximately 94.3% of cases, and multiple variations have been described for the remaining 5.7%. The purpose of this study was to review the literature on the pancreatic duct anatomy and to underline its importance in daily invasive endoscopic and surgical practice. Two main databases were searched for suitable articles published from 2000 to 2017, and results concerning more than 8,200 patients were included in the review. The most common anatomical variation was that of pancreas divisum, which appeared in approximately 4.5% of cases.
Olson, Nancy L. Galles
Noting the general agreement among reading authorities that one of the most effective methods of encouraging children to read comes from exposing them to children's literature and related activities, this paper presents an extensive literature review of materials dealing with the subject. Following an introductory section that details the goals…
Norman, Francesca F; Fanciulli, Chiara; Pérez-Molina, José-Antonio; Monge-Maillo, Begoña; López-Vélez, Rogelio
2016-01-01
Leprosy remains infrequent in non-endemic areas. The objective of this study was to describe the cases of leprosy reviewed at a referral unit for imported diseases in Europe and to compare these findings with published data on imported leprosy. Cases of leprosy evaluated at a referral centre are described and salient features of autochthonous and imported cases are compared. A review of the literature on imported leprosy was performed. During the study period, 25 patients with leprosy were followed-up (10 were autochthonous cases and 15 were considered to be imported). Regarding imported cases, the majority were diagnosed in Latin American immigrants (10/15, 67%), mean age was 42 years, there were no differences in gender distribution, estimated average time from arrival in Spain to first visit at the unit was 3 years and from symptom onset to diagnosis was 2 years. Over 80% of imported cases had multibacillary disease and over one third of patients had been previously diagnosed with leprosy. One third had received alternate incorrect diagnoses initially, leprosy completed standard therapy and were considered cured and over one third were lost to follow-up. Leprosy remains a complex disease for healthcare professionals unfamiliar with this infection. Manifestations are polymorphic so misdiagnoses and consequent delays in diagnosis are not infrequent and may lead to resulting disabilities. Early diagnosis and management are essential to prevent sequelae and possible transmission. Improving access to health care, especially for vulnerable groups, would be necessary to advance in the control of this disease. Copyright © 2016 Elsevier Ltd. All rights reserved.
Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.
2017-01-01
Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...
Edward, Jean; Carreon, Leah Yacat; Williams, Mark V; Glassman, Steven; Li, Jing
2018-02-01
Health literacy (HL) and the overall ability of patients to seek, understand, and apply health information play an important role in the management of chronic pain conditions. Awareness of how patients' HL skills influence their pain experience and how their ability to understand the treatment regimen and to manage chronic pain may allow physicians to adjust clinical treatment accordingly. Despite the prevalence and the substantial economic impact of chronic low back pain (LBP), little is known about the relationship between HL and the treatment and management of this common disease entity. The purpose of this systematic review of published research was to examine the importance and the implications of HL in the treatment and management of LBP. A literature search was performed in Web of Science, PubMed, Cumulative Index to Nursing and Allied Health Literature, and PsychInfo using medical subject heading (MeSH) terms related to LBP, HL, and patient education, which yielded only three studies that directly addressed HL among patients suffering from LBP. We identified only a limited number of studies that focused specifically on HL in the LBP population that were included in this review. The majority of studies excluded from this review focused on patient levels of educational attainment and patient education programs without addressing patients' HL levels and their impact on adherence to educational programs, self-care management, and rehabilitation, among other factors. The three studies that are critically reviewed in this review either use a direct measure of HL or make an effort to address HL in their programs. All three studies emphasize the importance of considering the HL of patients in the treatment and management of LBP. Building on these studies and the narrative review of other relevant literature, we identified significant gaps in current research addressing HL in the treatment and management of LBP. We developed recommendations for future research based
Tax Evasion, Information Reporting, and the Regressive Bias Hypothesis
DEFF Research Database (Denmark)
Boserup, Simon Halphen; Pinje, Jori Veng
A robust prediction from the tax evasion literature is that optimal auditing induces a regressive bias in effective tax rates compared to statutory rates. If correct, this will have important distributional consequences. Nevertheless, the regressive bias hypothesis has never been tested empirically...
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
Alexandrowicz, Rainer W; Jahn, Rebecca; Friedrich, Fabian; Unger, Anne
2016-06-01
Various studies have shown that caregiving relatives of schizophrenic patients are at risk of suffering from depression. These studies differ with respect to the applied statistical methods, which could influence the findings. Therefore, the present study analyzes to which extent different methods may cause differing results. The present study contrasts by means of one data set the results of three different modelling approaches, Rasch Modelling (RM), Structural Equation Modelling (SEM), and Linear Regression Modelling (LRM). The results of the three models varied considerably, reflecting the different assumptions of the respective models. Latent trait models (i. e., RM and SEM) generally provide more convincing results by correcting for measurement error and the RM specifically proves superior for it treats ordered categorical data most adequately.
Villanueva, Lidón; Montoya-Castilla, Inmaculada; Prado-Gascó, Vicente
2017-07-01
The purpose of this study is to analyze the combined effects of trait emotional intelligence (EI) and feelings on healthy adolescents' stress. Identifying the extent to which adolescent stress varies with trait emotional differences and the feelings of adolescents is of considerable interest in the development of intervention programs for fostering youth well-being. To attain this goal, self-reported questionnaires (perceived stress, trait EI, and positive/negative feelings) and biological measures of stress (hair cortisol concentrations, HCC) were collected from 170 adolescents (12-14 years old). Two different methodologies were conducted, which included hierarchical regression models and a fuzzy-set qualitative comparative analysis (fsQCA). The results support trait EI as a protective factor against stress in healthy adolescents and suggest that feelings reinforce this relation. However, the debate continues regarding the possibility of optimal levels of trait EI for effective and adaptive emotional management, particularly in the emotional attention and clarity dimensions and for female adolescents.
Fox, William
2012-01-01
The purpose of our modeling effort is to predict future outcomes. We assume the data collected are both accurate and relatively precise. For our oscillating data, we examined several mathematical modeling forms for predictions. We also examined both ignoring the oscillations as an important feature and including the oscillations as an important…
Bent, Sarah; McShea, Lynzee; Brennan, Siobhan
2015-01-01
Background: Hearing loss has a significant impact on living well and on communication in all adults, with the numbers affected increasing with age, and adults with learning disabilities being at particular risk. Methods: A review of the literature on hearing loss in older adults with learning disabilities was completed. Results: A significant…
Directory of Open Access Journals (Sweden)
Lidiia Kh. Oorzhak
2018-03-01
Full Text Available The authors of the article start off by expressing their concern about the level of command of Tuvan by the younger generation, especially children. Preserving and developing Tuvan language is impossible without literature in Tuvan and teaching it in schools and other educational institutions. The article deals with the issues of teaching Tuvan literature in secondary comprehensive schools of the Republic of Tuva. The authors also provide an overview of textbooks of Tuvan literature compiled at the laboratory of Tuvan philology, Institute for the Development of National Schools of the Republic of Tuva, in compliance with the Federal educational standards of Russian Federation. The textbook provide the mandatory minimum of the standard-provided content of general education and guarantee the required quality of knowledge for school graduates. In 2013-2017, textbooks titled «Tөreen chogaal» (Literature in the Native Tongue were compiled and published for Grades 5-9, as well as two accompanying textbooks for Grades 5 and 6. The textbooks rely on the methodological principles of the study program «Tyva aas chogaaly bolgash literatura. Niiti өөredilge cherleriniң 5-11 klasstarynga chizhek programma» (Tuvan folklore and literature. Sample Study Program for Grade 5-11 of Comprehensive Schools. In comparison to the previous generation of textbooks, these have been largely updated both in their structure and scope of its content. The texts were grouped in the following categories: “Folklore, the nation’s boundless treasury”, “From folklore to literary genres”, “The world of childhood”, “The world of wonders”, “Holy places”, “The Stars of Victory” and “Animal world”. They prominently feature folklore texts, including shaman songs; tests and creative tasks have also been developed. In terms of their content and methodology, the textbooks intend to familiarize students with the spiritual, moral and aesthetic values of
Ruiz-Colón, Kazandra; Chavez-Arias, Carlos; Díaz-Alcalá, José Eric; Martínez, María A
2014-07-01
Xylazine is not a controlled substance; it is marketed as a veterinary drug and used as a sedative, analgesic and muscle relaxant. In humans, it could cause central nervous system depression, respiratory depression, bradycardia, hypotension, and even death. There have been publications of 43 cases of xylazine intoxication in humans, in which 21 (49%) were non-fatal scenarios and 22 (51%) resulted in fatalities. Most of the non-fatal cases required medical intervention. Over recent years xylazine has emerged as an adulterant in recreational drugs, such as heroin or speedball (a cocaine and heroin mixture). From the 43 reported cases, 17 (40%) were associated with the use of xylazine as an adulterant of drugs of abuse. Its chronic use is reported to be associated with physical deterioration and skin ulceration. Literature shows some similar pharmacologic effects between xylazine and heroin in humans. These similar pharmacologic effects may create synergistic toxic effects in humans. Therefore, fatalities among drug users may increase due to the use of xylazine as an adulterant. Xylazine alone has proven harmful to humans and even more when it is combined with drugs of abuse. A comprehensive review of the literature of non-fatal and fatal xylazine intoxication cases including those in which the substance was used as adulterant is presented, in order to increase the awareness in the forensic community, law enforcement, and public health agencies. Published by Elsevier Ireland Ltd.
Regis, R R; Alves, C C S; Rocha, S S M; Negreiros, W A; Freitas-Pontes, K M
2016-10-01
The literature has questioned the real need for some clinical and laboratory procedures considered essential for achieving better results for complete denture fabrication. The aim of this study was to review the current literature concerning the relevance of a two-step impression procedure to achieve better clinical results in fabricating conventional complete dentures. Through an electronic search strategy of the PubMed/MEDLINE database, randomised controlled clinical trials which compared complete denture fabrication in adults in which one or two steps of impressions occurred were identified. The selections were made by three independent reviewers. Among the 540 titles initially identified, four studies (seven published papers) reporting on 257 patients evaluating aspects such as oral health-related quality of life, patient satisfaction with dentures in use, masticatory performance and chewing ability, denture quality, direct and indirect costs were considered eligible. The quality of included studies was assessed according to the Cochrane guidelines. The clinical studies considered for this review suggest that a two-step impression procedure may not be mandatory for the success of conventional complete denture fabrication regarding a variety of clinical aspects of denture quality and patients' perceptions of the treatment. © 2016 John Wiley & Sons Ltd.
Directory of Open Access Journals (Sweden)
Sina Babazadeh
2009-11-01
Full Text Available Ligament balancing affects many of the postoperative criteria for a successful knee replacement. A balanced knee contributes to improved alignment and stability. Ligament balancing helps reduce wear and loosening of the joint. A patient with a balanced knee is more likely to have increased range of motion and proprioception, and decreased pain. All these factors help minimize the need for revision surgery. Complications associated with ligament balancing can include instability caused by over-balancing and the possibility of neurovascular damage during or as a result of ligament balancing. This article attempts to summarize the literature, to define a balanced knee, and outline the benefits and possible complications of ligament balancing. Different techniques, sequences, and tools used in ligament balancing, and their relevance in correcting various deformities are reviewed.
Venskutonis, Tadas; Plotino, Gianluca; Juodzbalys, Gintaras; Mickevičienė, Lina
2014-12-01
To obtain essential information in clinical endodontics, cone-beam computed tomographic (CBCT) imaging can be used in all phases of treatment including diagnosis, treatment planning, during the treatment phase, and through post-treatment assessment and follow-up. The purpose of this article was to review the use of CBCT imaging in the diagnosis, treatment planning, and assessing the outcome of endodontic complications. Literature was selected through a search of PubMed electronic databases for the following keywords: tooth root injuries, tooth root radiography, tooth root perforation, tomography, cone-beam computed tomography, endodontic complications, tooth root internal/external resorption, root fractures, and broken instruments. The research was restricted to articles published in English. One hundred twelve articles met the inclusion criteria and were included in this review. Currently, intraoral radiography is the imaging technique of choice for the management of endodontic disease, but CBCT imaging appears to have a superior validity and reliability in the management of endodontic diagnosis and complications. Endodontic cases should be judged individually, and CBCT imaging should be considered in situations in which information from conventional imaging systems may not yield an adequate amount of information to allow the appropriate management of endodontic problems. CBCT imaging has the potential to become the first choice for endodontic treatment planning and outcome assessment, especially when new scanners with lower radiation doses will be available. Copyright © 2014 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Christopher Fitzpatrick
2016-12-01
Full Text Available Advocacy around mass treatment for the elimination of selected Neglected Tropical Diseases (NTDs has typically put the cost per person treated at less than US$ 0.50. Whilst useful for advocacy, the focus on a single number misrepresents the complexity of delivering "free" donated medicines to about a billion people across the world. We perform a literature review and meta-regression of the cost per person per round of mass treatment against NTDs. We develop a web-based software application (https://healthy.shinyapps.io/benchmark/ to calculate setting-specific unit costs against which programme budgets and expenditures or results-based pay-outs can be benchmarked.We reviewed costing studies of mass treatment for the control, elimination or eradication of lymphatic filariasis, schistosomiasis, soil-transmitted helminthiasis, onchocerciasis, trachoma and yaws. These are the main 6 NTDs for which mass treatment is recommended. We extracted financial and economic unit costs, adjusted to a standard definition and base year. We regressed unit costs on the number of people treated and other explanatory variables. Regression results were used to "predict" country-specific unit cost benchmarks.We reviewed 56 costing studies and included in the meta-regression 34 studies from 23 countries and 91 sites. Unit costs were found to be very sensitive to economies of scale, and the decision of whether or not to use local volunteers. Financial unit costs are expected to be less than 2015 US$ 0.50 in most countries for programmes that treat 100 thousand people or more. However, for smaller programmes, including those in the "last mile", or those that cannot rely on local volunteers, both economic and financial unit costs are expected to be higher.The available evidence confirms that mass treatment offers a low cost public health intervention on the path towards universal health coverage. However, more costing studies focussed on elimination are needed. Unit cost
Joo, Sung-Pil; Kim, Tae-Sun
2017-01-01
Clipping for intracranial aneurysms is done to achieve complete occlusion of the aneurysm without a remnant sac. Despite modern advancements of neurosurgical techniques, morbidity related to the clipping of intracranial aneurysms still exists. Clip occlusion of a parent artery or small hidden perforators commonly leads to permanent neurological deficits, and is a serious and unwanted complication. Thus, preserving blood flow in the branches and perforators of a parent artery is very important for successful surgery without postoperative morbidity and mortality. The aim of this review article is to discuss the consequences of perforator injury and how to avoid this phenomenon in aneurysm surgeries using intraoperative monitoring devices.
Broadhurst, Kathleen; Harrington, Ann
2016-11-01
The purpose of this review was to investigate within the literature the link between transcendent phenomena and peaceful death. The objectives were firstly to acknowledge the importance of such experiences and secondly to provide supportive spiritual care to dying patients. Information surrounding the aforementioned concepts is underreported in the literature. The following 4 key themes emerged: spiritual comfort; peaceful, calm death; spiritual transformation; and unfinished business The review established the importance of transcendence phenomena being accepted as spiritual experiences by health care professionals. Nevertheless, health care professionals were found to struggle with providing spiritual care to patients who have experienced them. Such phenomena are not uncommon and frequently result in peaceful death. Additionally, transcendence experiences of dying patients often provide comfort to the bereaved, assisting them in the grieving process. © The Author(s) 2015.
Lombardi, Roberta; Menchini, Laura; Corneli, Teresa; Magistrelli, Andrea; Accinni, Antonella; Monti, Lidia; Tomà, Paolo
2014-03-01
Wandering spleen is a rare condition in children that is often caused by loss or weakening of the splenic ligaments. Its clinical presentation is variable; 64% of children with wandering spleen have splenic torsion as a complication. To provide up-to-date information on the diagnosis, clinical management and diagnostic imaging approaches for wandering spleen in infants and children and to underline the importance of color Doppler US and CT in providing important information for patient management. We report a series of three children with wandering spleen treated at our children's hospital over the last 6 years. All three underwent clinical evaluation, color Doppler US and CT and were surgically treated. We also reviewed 40 articles that included 55 patients younger than 18 years reported in the Medline database from 2002 to 2012. We correlated pathological data with imaging findings. Color Doppler US, the first imaging modality in investigating abdominal symptoms in children with suspected wandering spleen, yielded a diagnostic sensitivity of 54.9%, whereas CT achieved about 71.7%. Radiologic evaluation has a major role in confirming the diagnosis of a suspected wandering spleen and avoiding potentially life-threatening complications requiring immediate surgery.
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Lombardi, Roberta; Menchini, Laura; Corneli, Teresa; Magistrelli, Andrea; Monti, Lidia; Toma, Paolo [Bambino Gesu Pediatric Hospital, Department of Radiology, Rome (Italy); Accinni, Antonella [Bambino Gesu Pediatric Hospital, Department of General and Thoracic Surgery, Rome (Italy)
2014-03-15
Wandering spleen is a rare condition in children that is often caused by loss or weakening of the splenic ligaments. Its clinical presentation is variable; 64% of children with wandering spleen have splenic torsion as a complication. To provide up-to-date information on the diagnosis, clinical management and diagnostic imaging approaches for wandering spleen in infants and children and to underline the importance of color Doppler US and CT in providing important information for patient management. We report a series of three children with wandering spleen treated at our children's hospital over the last 6 years. All three underwent clinical evaluation, color Doppler US and CT and were surgically treated. We also reviewed 40 articles that included 55 patients younger than 18 years reported in the Medline database from 2002 to 2012. We correlated pathological data with imaging findings. Color Doppler US, the first imaging modality in investigating abdominal symptoms in children with suspected wandering spleen, yielded a diagnostic sensitivity of 54.9%, whereas CT achieved about 71.7%. Radiologic evaluation has a major role in confirming the diagnosis of a suspected wandering spleen and avoiding potentially life-threatening complications requiring immediate surgery. (orig.)
International Nuclear Information System (INIS)
Lombardi, Roberta; Menchini, Laura; Corneli, Teresa; Magistrelli, Andrea; Monti, Lidia; Toma, Paolo; Accinni, Antonella
2014-01-01
Wandering spleen is a rare condition in children that is often caused by loss or weakening of the splenic ligaments. Its clinical presentation is variable; 64% of children with wandering spleen have splenic torsion as a complication. To provide up-to-date information on the diagnosis, clinical management and diagnostic imaging approaches for wandering spleen in infants and children and to underline the importance of color Doppler US and CT in providing important information for patient management. We report a series of three children with wandering spleen treated at our children's hospital over the last 6 years. All three underwent clinical evaluation, color Doppler US and CT and were surgically treated. We also reviewed 40 articles that included 55 patients younger than 18 years reported in the Medline database from 2002 to 2012. We correlated pathological data with imaging findings. Color Doppler US, the first imaging modality in investigating abdominal symptoms in children with suspected wandering spleen, yielded a diagnostic sensitivity of 54.9%, whereas CT achieved about 71.7%. Radiologic evaluation has a major role in confirming the diagnosis of a suspected wandering spleen and avoiding potentially life-threatening complications requiring immediate surgery. (orig.)
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Regression in organizational leadership.
Kernberg, O F
1979-02-01
The choice of good leaders is a major task for all organizations. Inforamtion regarding the prospective administrator's personality should complement questions regarding his previous experience, his general conceptual skills, his technical knowledge, and the specific skills in the area for which he is being selected. The growing psychoanalytic knowledge about the crucial importance of internal, in contrast to external, object relations, and about the mutual relationships of regression in individuals and in groups, constitutes an important practical tool for the selection of leaders.
Sinha, Shashank S.; Sukul, Devraj; Lazarus, John J.; Polavarapu, Vivek; Chan, Paul S.; Neumar, Robert W.; Nallamothu, Brahmajee K.
2016-01-01
Background Cardiac arrests are a major public health concern worldwide. The extent and types of randomized controlled trials (RCTs) – our most reliable source of clinical evidence – conducted in these high-risk patients over recent years are largely unknown. Methods and Results We performed a systematic review, identifying all RCTs published in PubMed, EMBASE, Scopus, Web of Science, and the Cochrane Library from 1995 to 2014 that focused on acute treatment of non-traumatic cardiac arrest in adults. We then extracted data on the setting of study populations, types and timing of interventions studied, risk of bias, outcomes reported and how these factors have changed over time. Over this twenty-year period, 92 RCTs were published containing 64,309 patients (median, 225.5 per trial). Of these, 81 RCTs (88.0%) involved out-of-hospital cardiac arrest whereas 4 (4.3%) involved in-hospital cardiac arrest and 7 (7.6%) included both. Eighteen RCTs (19.6%) were performed in the U.S., 68 (73.9%) were performed outside the U.S., and 6 (6.5%) were performed in both settings. Thirty-eight RCTs (41.3%) evaluated drug therapy, 39 (42.4%) evaluated device therapy, and 15 (16.3%) evaluated protocol improvements. Seventy-four RCTs (80.4%) examined interventions during the cardiac arrest, 15 (16.3%) examined post-cardiac arrest treatment, and 3 (3.3%) studied both. Overall, reporting of risk of bias was limited. The most common outcome reported was ROSC: 86 (93.5%) with only 22 (23.9%) reporting survival beyond 6 months. Fifty-three RCTs (57.6%) reported global ordinal outcomes whereas 15 (16.3%) reported quality-of-life. RCTs in the last 5 years were more likely to be focused on protocol improvement and post-cardiac arrest care. Conclusions Important gaps in RCTs of cardiac arrest treatments exist, especially those examining in-hospital cardiac arrest, protocol improvement, post-cardiac arrest care, and long-term or quality-of-life outcomes. PMID:27756794
Multicollinearity and Regression Analysis
Daoud, Jamal I.
2017-12-01
In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
Differentiating regressed melanoma from regressed lichenoid keratosis.
Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A
2017-04-01
Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Regression in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
Developmental regression in autism: research and conceptual questions
Directory of Open Access Journals (Sweden)
Carolina Lampreia
2013-11-01
Full Text Available The subject of developmental regression in autism has gained importance and a growing number of studies have been conducted in recent years. It is a major issue indicating that there is not a unique form of autism onset. However the phenomenon itself and the concept of regression have been the subject of some debate: there is no consensus on the existence of regression, as there is no consensus on its definition. The aim of this paper is to review the research literature in this area and to introduce some conceptual questions about its existence and its definition.
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
Advanced statistics: linear regression, part I: simple linear regression.
Marill, Keith A
2004-01-01
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.
Abstract Expression Grammar Symbolic Regression
Korns, Michael F.
This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Adaptive Metric Kernel Regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
Logic regression and its extensions.
Schwender, Holger; Ruczinski, Ingo
2010-01-01
Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...
Background: Data reported in the published literature have been used qualitatively to aid exposure assessment activities in epidemiologic studies. Analyzing these data in computational models presents statistical challenges because these data are often reported as summary statist...
Wijnia, Lisette; Loyens, Sofie M.; Derous, Eva; Schmidt, Henk G.
2015-01-01
In problem-based learning students are responsible for their own learning process, which becomes evident when they must act independently, for example, when selecting literature resources for individual study. It is a matter of debate whether it is better to have students select their own literature resources or to present them with a list of…
Multicollinearity in applied economics research and the Bayesian linear regression
EISENSTAT, Eric
2016-01-01
This article revises the popular issue of collinearity amongst explanatory variables in the context of a multiple linear regression analysis, particularly in empirical studies within social science related fields. Some important interpretations and explanations are highlighted from the econometrics literature with respect to the effects of multicollinearity on statistical inference, as well as the general shortcomings of the once fervent search for methods intended to detect and mitigate thes...
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
Suppression Situations in Multiple Linear Regression
Shieh, Gwowen
2006-01-01
This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…
Advanced statistics: linear regression, part II: multiple linear regression.
Marill, Keith A
2004-01-01
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
DEFF Research Database (Denmark)
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
To show the importance of literature teaching in English language teaching (ELT),this paper explores the relations between language, culture and literature,examines the present problems in literature teaching and possible solutions are suggested as well.
Understanding logistic regression analysis
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...
Regression analysis with categorized regression calibrated exposure: some interesting findings
Directory of Open Access Journals (Sweden)
Hjartåker Anette
2006-07-01
Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Alternative Methods of Regression
Birkes, David
2011-01-01
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s
Fuchs, Joan; von Dechend, Margot; Mordasini, Raffaella; Ceschi, Alessandro; Nentwig, Wolfgang
2014-01-01
Literature on bird spider or tarantula bites (Theraphosidae) is rare. This is astonishing as they are coveted pets and interaction with their keepers (feeding, cleaning the terrarium or taking them out to hold) might increase the possibility for bites. Yet, this seems to be a rare event and might be why most theraphosids are considered to be harmless, even though the urticating hairs of many American species can cause disagreeable allergic reactions. We are describing a case of a verified bite by an Indian ornamental tree spider (Poecilotheria regalis), where the patient developed severe, long lasting muscle cramps several hours after the bite. We present a comprehensive review of the literature on bites of these beautiful spiders and conclude that a delayed onset of severe muscle cramps, lasting for days, is characteristic for Poecilotheria bites. We discuss Poecilotheria species as an exception from the general assumption that theraphosid bites are harmless to humans. Copyright © 2013 Elsevier Ltd. All rights reserved.
Dumas, P; Georgiou, C; Chignon-Sicard, B; Balaguer, T; Lebreton, E; Dumontier, C
2013-02-01
The intraosseous ganglion cyst (IOGC) is a benign and lytic bone tumor affecting mostly the metaphyseal and epiphyseal regions of long bones. Its location on the short bones, including the carpal bones has been little reported in the literature. Our review of the literature shows consensus about the surgical techniques to use, but there is currently no real consensus about its pathophysiology, and its diagnostic work-up. Complications related to this lesion (mainly the risk of pathologic fracture) are potentially serious, and can cause irreversible damage. They therefore require accurate assessment to guide the choice of medical or surgical treatment, including a CT scan, which - we believe - is essential. Copyright © 2012. Published by Elsevier SAS.
Sharma, Ketaki; Mudgil, Poonam; Whitehall, John S.; Gosbell, Iain
2017-01-01
Background Aggregatibacter actinomycetemcomitans most commonly causes periodontitis but has been reported to infect heart valves, soft tissue, brain and lungs, and distal bones. Osteomyelitis distal to the jaw is rarely described. Case presentation We report an unusual and rare case of chronic osteomyelitis caused by A. actinomycetemcomitans in the toe of a paediatric patient, and review the available literature. The infection was managed with intravenous antibiotics followed by oral antibiot...
Directory of Open Access Journals (Sweden)
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Understanding logistic regression analysis.
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Sharma, Ketaki; Mudgil, Poonam; Whitehall, John S; Gosbell, Iain
2017-03-14
Aggregatibacter actinomycetemcomitans most commonly causes periodontitis but has been reported to infect heart valves, soft tissue, brain and lungs, and distal bones. Osteomyelitis distal to the jaw is rarely described. We report an unusual and rare case of chronic osteomyelitis caused by A. actinomycetemcomitans in the toe of a paediatric patient, and review the available literature. The infection was managed with intravenous antibiotics followed by oral antibiotics. This is an unusual presentation of A. actinomycetemcomitans causing chronic osteomyelitis presumed due to nidation in a minimally damaged bone, associated with bacteraemia of an oral commensal. It occurred in the toe, without obvious dental predisposition; associated with minimal clinical disturbance and with muted immune response.
Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms
Directory of Open Access Journals (Sweden)
Zhongyi Hu
2013-01-01
Full Text Available Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA based memetic algorithm (FA-MA to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.
Understanding poisson regression.
Hayat, Matthew J; Higgins, Melinda
2014-04-01
Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.
Totton, Sarah C; Farrar, Ashley M; Wilkins, Wendy; Bucher, Oliver; Waddell, Lisa A; Wilhelm, Barbara J; McEwen, Scott A; Rajić, Andrijana
2012-10-01
Eating inappropriately prepared poultry meat is a major cause of foodborne salmonellosis. Our objectives were to determine the efficacy of feed and water additives (other than competitive exclusion and antimicrobials) on reducing Salmonella prevalence or concentration in broiler chickens using systematic review-meta-analysis and to explore sources of heterogeneity found in the meta-analysis through meta-regression. Six electronic databases were searched (Current Contents (1999-2009), Agricola (1924-2009), MEDLINE (1860-2009), Scopus (1960-2009), Centre for Agricultural Bioscience (CAB) (1913-2009), and CAB Global Health (1971-2009)), five topic experts were contacted, and the bibliographies of review articles and a topic-relevant textbook were manually searched to identify all relevant research. Study inclusion criteria comprised: English-language primary research investigating the effects of feed and water additives on the Salmonella prevalence or concentration in broiler chickens. Data extraction and study methodological assessment were conducted by two reviewers independently using pretested forms. Seventy challenge studies (n=910 unique treatment-control comparisons), seven controlled studies (n=154), and one quasi-experiment (n=1) met the inclusion criteria. Compared to an assumed control group prevalence of 44 of 1000 broilers, random-effects meta-analysis indicated that the Salmonella cecal colonization in groups with prebiotics (fructooligosaccharide, lactose, whey, dried milk, lactulose, lactosucrose, sucrose, maltose, mannanoligosaccharide) added to feed or water was 15 out of 1000 broilers; with lactose added to feed or water it was 10 out of 1000 broilers; with experimental chlorate product (ECP) added to feed or water it was 21 out of 1000. For ECP the concentration of Salmonella in the ceca was decreased by 0.61 log(10)cfu/g in the treated group compared to the control group. Significant heterogeneity (Cochran's Q-statistic p≤0.10) was observed
Janse, Julienne A; Sie-Go, Daisy M D S; Schreuder, Henk W R
2011-06-17
Cases of cervical carcinoma metastasing to the transposed ovary are rarely reported in the literature. In this report, the authors present the case of a 53-year-old woman with a persisting, unsuspected cyst in the right transposed ovary, 10 years after treatment for adenosquamous carcinoma of the cervix. It is the first report describing a secondary ovarian malignancy originating from a cervical adenosquamous carcinoma in a transposed ovary. In addition, this is the first account of an ovarian metastasis 10 years after primary treatment for cervical cancer. Furthermore, pathologic examination with immunohistochemistry and human papillomavirus genotyping played a key role in the diagnostic process, as the case did not raise suspicion by ultrasound findings neither by cytological examination after cytological aspiration or by appearance during surgery.
Aka, Kacou Edele; Apollinaire Horo, Gninlgninrin; Fomba, Minata; Kouyate, Salif; Koffi, Abdoul Koffi; Konan, Seni; Fanny, Mohamed; Effi, Benjamin; Kone, Mamourou
2017-01-01
The cavernous hemangioma is a rare benign vascular tumor. About 50 cases of this disease were found in the literature over the last century and only 9 cases of cavernous hemangioma on the pregnant uterus were published it comes into cavernous or capillary form. The symptomatology is not unequivocal and when it occurs during pregnancy or postpartum, it causes life-threatening cataclysmic hemorrhage. Antenatal diagnosis is difficult and requires a multidisciplinary approach with pathologists, radiologists and gynecologists to avoid these complications or unnecessary hysterectomies. The diagnosis is histological. Hysterectomy is possible after failure of conservative treatment means. We report a rare case, a novel mixed cavernous hemangioma of the body associated with a capillary hemangioma of the cervix in a patient of 28 years 5th visors with recurrent genital bleeding in the postpartum period leading to a hysterectomy.
Morris, Jacqui; Oliver, Tracey; Kroll, Thilo; Macgillivray, Steve
2012-01-01
Background. People with stroke are not maintaining adequate engagement in physical activity (PA) for health and functional benefit. This paper sought to describe any psychological and social factors that may influence physical activity engagement after stroke. Methods. A structured literature review of studies indexed in MEDLINE, CinAHL, P&BSC, and PsycINFO using search terms relevant to stroke, physical disabilities, and PA. Publications reporting empirical findings (quantitative or qualitative) regarding psychological and/or social factors were included. Results. Twenty studies from 19 publications (9 surveys, 1 RCT, and 10 qualitative studies) were included. Seventeen studies reported findings pertinent to psychological factors and fourteen findings pertinent to social factors. Conclusion. Self-efficacy, physical activity beliefs, and social support appear particularly relevant to physical activity behaviour after stroke and should be included in theoretically based physical interventions. The Transtheoretical Model and the Theory of Planned Behaviour are candidate behavioural models that may support intervention development.
Directory of Open Access Journals (Sweden)
Mok Tik
2014-06-01
Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.
DEFF Research Database (Denmark)
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....
Elliott, Robert E; Tanweer, Omar
2014-01-01
We reviewed published radiographic and cadaver series describing the incidence of the anatomical anomaly ponticulus posticus and discuss its relevance to C1 lateral mass screw (C1LMS) insertion. Online databases were searched for English-language articles describing the presence of ponticulus posticus in cadaver and radiographic studies. Forty-four reports describing 21,789 patients (n = 15,542) or bony/cadaver specimens (n = 6247) fulfilled inclusion criteria. Meta-analysis techniques were applied to estimate the prevalence of this anomaly. The overall prevalence of ponticulus posticus was 16.7%. The anomaly was identified in 18.8% of cadaver, 17.2% of computed tomographic, and 16.6% on radiographic studies. The anomaly composed a complete foramen in 9.3% of patients and was partial/incomplete in 8.7%. It was present bilaterally in 5.4% of cases and unilateral in 7.6%. There was no significant difference in prevalence between males (15.8%) and females (14.6%). Review of that literature demonstrated a dramatic increase in the number of patients treated with C1LMS through the posterior arch since first described in 2002, necessitating recognition of this anomaly when performing the Goel-Harms procedure. The atlantal anomaly ponticulus posticus is not rare, occurring in 16.7% of patients in radiographic and cadaver studies. This anomaly may give the false impression that the posterior arch of the atlas is of adequate size to accommodate a C1LMS and may lead to inadvertent vertebral artery injury. Careful assessment via preoperative multiplanar computed tomographic imaging should be performed before consideration of C1LMS implantation. Copyright © 2014 Elsevier Inc. All rights reserved.
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bounded Gaussian process regression
DEFF Research Database (Denmark)
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...... with the proposed explicit noise-model extension....
and Multinomial Logistic Regression
African Journals Online (AJOL)
This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
Ranalli, Anthony J.; Macalady, Donald L.
2010-01-01
We reviewed published studies from primarily glaciated regions in the United States, Canada, and Europe of the (1) transport of nitrate from terrestrial ecosystems to aquatic ecosystems, (2) attenuation of nitrate in the riparian zone of undisturbed and agricultural watersheds, (3) processes contributing to nitrate attenuation in riparian zones, (4) variation in the attenuation of nitrate in the riparian zone, and (5) importance of in-stream and hyporheic processes for nitrate attenuation in the stream channel. Our objectives were to synthesize the results of these studies and suggest methodologies to (1) monitor regional trends in nitrate concentration in undisturbed 1st order watersheds and (2) reduce nitrate loads in streams draining agricultural watersheds. Our review reveals that undisturbed headwater watersheds have been shown to be very retentive of nitrogen, but the importance of biogeochemical and hydrological riparian zone processes in retaining nitrogen in these watersheds has not been demonstrated as it has for agricultural watersheds. An understanding of the role of the riparian zone in nitrate attenuation in undisturbed watersheds is crucial because these watersheds are increasingly subject to stressors, such as changes in land use and climate, wildfire, and increases in atmospheric nitrogen deposition. In general, understanding processes controlling the concentration and flux of nitrate is critical to identifying and mapping the vulnerability of watersheds to water quality changes due to a variety of stressors. In undisturbed and agricultural watersheds we propose that understanding the importance of riparian zone processes in 2nd order and larger watersheds is critical. Research is needed that addresses the relative importance of how the following sources of nitrate along any given stream reach might change as watersheds increase in size and with flow: (1) inputs upstream from the reach, (2) tributary inflow, (3) water derived from the riparian zone
Duesterhoeft, Sara M; Ernst, Linda M; Siebert, Joseph R; Kapur, Raj P
2007-12-15
Sirenomelia and caudal regression have sparked centuries of interest and recent debate regarding their classification and pathogenetic relationship. Specific anomalies are common to both conditions, but aside from fusion of the lower extremities, an aberrant abdominal umbilical artery ("persistent vitelline artery") has been invoked as the chief anatomic finding that distinguishes sirenomelia from caudal regression. This observation is important from a pathogenetic viewpoint, in that diversion of blood away from the caudal portion of the embryo through the abdominal umbilical artery ("vascular steal") has been proposed as the primary mechanism leading to sirenomelia. In contrast, caudal regression is hypothesized to arise from primary deficiency of caudal mesoderm. We present five cases of caudal regression that exhibit an aberrant abdominal umbilical artery similar to that typically associated with sirenomelia. Review of the literature identified four similar cases. Collectively, the series lends support for a caudal regression-sirenomelia spectrum with a common pathogenetic basis and suggests that abnormal umbilical arterial anatomy may be the consequence, rather than the cause, of deficient caudal mesoderm. (c) 2007 Wiley-Liss, Inc.
Advanced colorectal neoplasia risk stratification by penalized logistic regression.
Lin, Yunzhi; Yu, Menggang; Wang, Sijian; Chappell, Richard; Imperiale, Thomas F
2016-08-01
Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. © The Author(s) 2013.
Ridge Regression Signal Processing
Kuhl, Mark R.
1990-01-01
The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.
Directory of Open Access Journals (Sweden)
T. Robyns, MD.
2014-05-01
Full Text Available Mutations in the SCN5A gene are responsible for multiple phenotypical presentations including Brugada syndrome, long QT syndrome, progressive familial heart block, sick sinus syndrome, dilated cardiomyopathy, lone atrial fibrillation and multiple overlap syndromes. These different phenotypic expressions of a mutation in a single gene can be explained by variable expression and reduced penetrance. One of the possible explanations of these phenomena is the co-inheritance of genetic variants. We describe a family where the individuals exhibit a compound heterozygosity in the SCN5A gene including a mutation (R1632H and a new variant (M858L. Individuals with both the mutation and new variant present with a more severe phenotype including spontaneous atrial tachyarrhythmia at young age. We give an overview of the different phenotypes of "SCN5A disease" and discuss the importance of co-inherited genetic variants in the expression of SCN5A disease.
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Spontaneous regression of pulmonary bullae
International Nuclear Information System (INIS)
Satoh, H.; Ishikawa, H.; Ohtsuka, M.; Sekizawa, K.
2002-01-01
The natural history of pulmonary bullae is often characterized by gradual, progressive enlargement. Spontaneous regression of bullae is, however, very rare. We report a case in which complete resolution of pulmonary bullae in the left upper lung occurred spontaneously. The management of pulmonary bullae is occasionally made difficult because of gradual progressive enlargement associated with abnormal pulmonary function. Some patients have multiple bulla in both lungs and/or have a history of pulmonary emphysema. Others have a giant bulla without emphysematous change in the lungs. Our present case had treated lung cancer with no evidence of local recurrence. He had no emphysematous change in lung function test and had no complaints, although the high resolution CT scan shows evidence of underlying minimal changes of emphysema. Ortin and Gurney presented three cases of spontaneous reduction in size of bulla. Interestingly, one of them had a marked decrease in the size of a bulla in association with thickening of the wall of the bulla, which was observed in our patient. This case we describe is of interest, not only because of the rarity with which regression of pulmonary bulla has been reported in the literature, but also because of the spontaneous improvements in the radiological picture in the absence of overt infection or tumor. Copyright (2002) Blackwell Science Pty Ltd
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
Directory of Open Access Journals (Sweden)
Nasim Jafaripozve
2013-01-01
Full Text Available The keratocystic odontogenic tumor (KCOT is a relatively common oral and maxillofacial lesion with specific characteristics such us rapid growth, extension into the surrounding tissues and high rates of recurrence. Various treatment modalities have been reported. Due to the very thin and friable lining characteristic of the tumor, enucleation can be difficult undertaken and for this reason it is associated with the highest recurrence rates. A 22-year-old male referred to our clinic due to a slight expansion in the right mandible from 2 years ago. He has a history of occurrence of KCOT in this region that was treated surgically by enucleation and curettage 5 years ago. Cone beam computed tomography showed a multilocular radiolucent lesion that extended from the angle of the mandible to the symphysis. Incisional biopsy showed a KCOT recurrence that surgically treated with resection of the right mandible by continuity preservation. Selection of the best treatment modality and also a periodical lifelong follow-up is very important to reduce the rate of recurrence and morbidity of the patient.
Forecasting urban water demand: A meta-regression analysis.
Sebri, Maamar
2016-12-01
Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.
Model building strategy for logistic regression: purposeful selection.
Zhang, Zhongheng
2016-03-01
Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.
Regression Analysis and the Sociological Imagination
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Steganalysis using logistic regression
Lubenko, Ivans; Ker, Andrew D.
2011-02-01
We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.
SEPARATION PHENOMENA LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Ikaro Daniel de Carvalho Barreto
2014-03-01
Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...
Pathological assessment of liver fibrosis regression
Directory of Open Access Journals (Sweden)
WANG Bingqiong
2017-03-01
Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.
Variable and subset selection in PLS regression
DEFF Research Database (Denmark)
Høskuldsson, Agnar
2001-01-01
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...
DEFF Research Database (Denmark)
Hansen, Henrik; Tarp, Finn
2001-01-01
This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy....... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes.......This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy...
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun
2016-07-01
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
The process and utility of classification and regression tree methodology in nursing research.
Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda
2014-06-01
This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Discussion paper. English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984-2013. Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. © 2013 The Authors. Journal of Advanced Nursing Published by John Wiley & Sons Ltd.
Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
Polynomial regression analysis and significance test of the regression function
International Nuclear Information System (INIS)
Gao Zhengming; Zhao Juan; He Shengping
2012-01-01
In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
Spontaneous regression of metastases from malignant melanoma: a case report
DEFF Research Database (Denmark)
Kalialis, Louise V; Drzewiecki, Krzysztof T; Mohammadi, Mahin
2008-01-01
A case of a 61-year-old male with widespread metastatic melanoma is presented 5 years after complete spontaneous cure. Spontaneous regression occurred in cutaneous, pulmonary, hepatic and cerebral metastases. A review of the literature reveals seven cases of regression of cerebral metastases; thi...
Augmenting Data with Published Results in Bayesian Linear Regression
de Leeuw, Christiaan; Klugkist, Irene
2012-01-01
In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this…
Combining Alphas via Bounded Regression
Directory of Open Access Journals (Sweden)
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Time-adaptive quantile regression
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation
Directory of Open Access Journals (Sweden)
Sharad Damodar Gore
2009-10-01
Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Optimising import phytosanitary inspection
Surkov, I.
2007-01-01
Keywords: quarantine pest, plant health policy, optimization, import phytosanitary inspection, ‘reduced checks’, optimal allocation of resources, multinomial logistic regression, the Netherlands World trade is a major vector of spread of quarantine plant pests. Border phytosanitary inspection
Spontaneous regression of metastases from malignant melanoma: a case report
DEFF Research Database (Denmark)
Kalialis, Louise V; Drzewiecki, Krzysztof T; Mohammadi, Mahin
2008-01-01
A case of a 61-year-old male with widespread metastatic melanoma is presented 5 years after complete spontaneous cure. Spontaneous regression occurred in cutaneous, pulmonary, hepatic and cerebral metastases. A review of the literature reveals seven cases of regression of cerebral metastases......; this report is the first to document complete spontaneous regression of cerebral metastases from malignant melanoma by means of computed tomography scans. Spontaneous regression is defined as the partial or complete disappearance of a malignant tumour in the absence of all treatment or in the presence...
Ernst, Anja F.; Albers, Casper J.
2017-01-01
Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated
Regression Models For Multivariate Count Data.
Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei
2017-01-01
Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.
Post-processing through linear regression
van Schaeybroeck, B.; Vannitsem, S.
2011-03-01
Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Post-processing through linear regression
Directory of Open Access Journals (Sweden)
B. Van Schaeybroeck
2011-03-01
Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.
These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Regression Discontinuity Designs Based on Population Thresholds
DEFF Research Database (Denmark)
Eggers, Andrew C.; Freier, Ronny; Grembi, Veronica
In many countries, important features of municipal government (such as the electoral system, mayors' salaries, and the number of councillors) depend on whether the municipality is above or below arbitrary population thresholds. Several papers have used a regression discontinuity design (RDD...
Predicting Social Trust with Binary Logistic Regression
Adwere-Boamah, Joseph; Hufstedler, Shirley
2015-01-01
This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…
Panel Smooth Transition Regression Models
DEFF Research Database (Denmark)
González, Andrés; Terasvirta, Timo; Dijk, Dick van
We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Testing discontinuities in nonparametric regression
Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun
2017-01-01
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Regression analysis for the social sciences
Gordon, Rachel A
2010-01-01
The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
International Nuclear Information System (INIS)
Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei
2007-01-01
Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age
Tumor regression patterns in retinoblastoma
International Nuclear Information System (INIS)
Zafar, S.N.; Siddique, S.N.; Zaheer, N.
2016-01-01
To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)
Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul
2015-11-04
Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.
Directory of Open Access Journals (Sweden)
Melissa E. Stauffer
2011-01-01
Full Text Available Our objective was to develop a working definition of nonresponse to analgesic treatment of arthritis, focusing on the measurement of pain on the 0–100 mm pain visual analog scale (VAS. We reviewed the literature to assess the smallest detectable difference (SDD, the minimal detectable change (MDC, and the minimal clinically important difference (MCID. The SDD for improvement reported in three studies of rheumatoid arthritis was 18.6, 19.0, and 20.0. The median MDC was 25.4 for 7 studies of osteoarthritis and 5 studies of rheumatoid arthritis (calculated for a reliability coefficient of 0.85. The MCID increased with increasing baseline pain score. For baseline VAS tertiles defined by scores of 30–49, 50–65, and >65, the MCID for improvement was, respectively, 7–11 units, 19–27 units, and 29–37 units. Nonresponse can thus be defined in terms of the MDC for low baseline pain scores and in terms of the MCID for high baseline scores.
Meila, Dan; Saliou, Guillaume; Krings, Timo
2015-01-01
Despite the variable anatomy of the anterior communicating artery (AcoA) complex, three main perforating branches can be typically identified the largest of which being the subcallosal artery (ScA). We present a case series of infarction in the vascular territory of the ScA to highlight the anatomy, the clinical symptomatology, and the presumed pathophysiology as it pertains to endovascular and surgical management of vascular pathology in this region. In this retrospective multicenter case series study of patients who were diagnosed with symptomatic ScA stroke, we analyzed all available clinical records, MRI, and angiographic details. Additionally, a review of the literature is provided. We identified five different cases of ScA stroke, leading to a subsequent infarction of the fornix and the genu of the corpus callosum. The presumed pathophysiology in non-iatrogenic cases is microangiopathy, rather than embolic events; iatrogenic SCA occlusion can present after both surgical and endovascular treatment of AcoA aneurysms that may occur with or without occlusion of the AcoA. Stroke in the vascular territory of the ScA leads to a characteristic imaging and clinical pattern. Ischemia involves the anterior columns of the fornix and the genu of the corpus callosum, and patients present with a Korsakoff's syndrome including disturbances of short-term memory and cognitive changes. We conclude that despite its small size, the ScA is an important artery to watch out for during surgical or endovascular treatment of AcoA aneurysms.
Regression to Causality : Regression-style presentation influences causal attribution
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...
Linear regression analysis: part 14 of a series on evaluation of scientific publications.
Schneider, Astrid; Hommel, Gerhard; Blettner, Maria
2010-11-01
Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.
Quantile Regression With Measurement Error
Wei, Ying; Carroll, Raymond J.
2009-01-01
. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a
From Rasch scores to regression
DEFF Research Database (Denmark)
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
Testing Heteroscedasticity in Robust Regression
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2011-01-01
Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Is past life regression therapy ethical?
Andrade, Gabriel
2017-01-01
Past life regression therapy is used by some physicians in cases with some mental diseases. Anxiety disorders, mood disorders, and gender dysphoria have all been treated using life regression therapy by some doctors on the assumption that they reflect problems in past lives. Although it is not supported by psychiatric associations, few medical associations have actually condemned it as unethical. In this article, I argue that past life regression therapy is unethical for two basic reasons. First, it is not evidence-based. Past life regression is based on the reincarnation hypothesis, but this hypothesis is not supported by evidence, and in fact, it faces some insurmountable conceptual problems. If patients are not fully informed about these problems, they cannot provide an informed consent, and hence, the principle of autonomy is violated. Second, past life regression therapy has the great risk of implanting false memories in patients, and thus, causing significant harm. This is a violation of the principle of non-malfeasance, which is surely the most important principle in medical ethics.
Directory of Open Access Journals (Sweden)
Horst Entorf
2015-07-01
Full Text Available Two alternative hypotheses – referred to as opportunity- and stigma-based behavior – suggest that the magnitude of the link between unemployment and crime also depends on preexisting local crime levels. In order to analyze conjectured nonlinearities between both variables, we use quantile regressions applied to German district panel data. While both conventional OLS and quantile regressions confirm the positive link between unemployment and crime for property crimes, results for assault differ with respect to the method of estimation. Whereas conventional mean regressions do not show any significant effect (which would confirm the usual result found for violent crimes in the literature, quantile regression reveals that size and importance of the relationship are conditional on the crime rate. The partial effect is significantly positive for moderately low and median quantiles of local assault rates.
Directory of Open Access Journals (Sweden)
Leonardo de Souza Vasconcellos
2005-04-01
Full Text Available As hematúrias são achados comuns nos exames de urina de rotina e nem sempre são sinais de doenças. A presença de hematúria associada a outras alterações urinárias, especialmente a proteinúria, sugere comprometimento do trato urinário e merece investigação. Na literatura são inúmeros os trabalhos que valorizam a sedimentoscopia urinária, principalmente a morfologia das hemácias, como indicativo do local do sangramento: se glomerular ou não-glomerular. Neste artigo, os autores revisam o estudo do dismorfismo eritrocitário, enfatizando a definição, a fisiopatologia, os métodos, os valores de referência e as limitações apontadas na literatura. As comparações com demais marcadores de hemorragia glomerular também foram discutidas. No final, os autores relatam como a literatura interpreta e utiliza os resultados da pesquisa do dismorfismo eritrocitário para guiar a propedêutica complementar na investigação da origem da hematúria.The hematurias are a frequent finding in urinary routine exams and do not necessarily indicate illness. The presence of hematuria associated with other urinary disturbances, especially proteinuria, indicates urinary tract pathology and should be investigated. In the literature, several studies point out the importance of urinary sedimentoscopy, specially the red cells morphology, to determine the source of bleeding: glomerular or non-glomerular. In this article, the authors review the dysmorphic erythrocyte, emphasizing on the meaning, the physiopathology, the methods, the cut-of values, and the limitations according to the literature. The comparisons with other markers of glomerular bleeding were also discussed. At the end, the authors exposed how the literature analyses and applies the dysmorphic erythrocyte results to guide the complementary research for investigation the source of urinary bleeding.
International Nuclear Information System (INIS)
Gomez Cobo, A.
1997-01-01
The presentation discusses the following: general concepts of importance measures; example fault tree, used to illustrate importance measures; Birnbaum's structural importance; criticality importance; Fussel-Vesely importance; upgrading function; risk achievement worth; risk reduction worth
Logistic regression for dichotomized counts.
Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W
2016-12-01
Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.
Directory of Open Access Journals (Sweden)
Dragana Litričin Dunić
2015-04-01
Full Text Available Literature can represent, on the one hand, the establishment of cultural and national identity, and, on the other hand, a constant indicator of the differences. Self-image and the image of the Other in literature is very important not only for understanding national character and preservation of cultural identity, but also for the release from ideological reading and stereotyping. Analyzing the image of the Other, research into the representation of the Balkans symbolically represents in the popular literature of the West, study of the cultural context and the processes that formed the writer’s perceptions that determine the establishment of stereotypes about Homo Balcanicus and many others, are all important tasks of imagological research, as well as the key research tasks conducted nowadays. In this paper we shall discuss some of these issues in the field of comparative literature.
Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies
Vatcheva, Kristina P.; Lee, MinJae; McCormick, Joseph B.; Rahbar, Mohammad H.
2016-01-01
The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epide...
Producing The New Regressive Left
DEFF Research Database (Denmark)
Crone, Christine
members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...
A Matlab program for stepwise regression
Directory of Open Access Journals (Sweden)
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
Regression filter for signal resolution
International Nuclear Information System (INIS)
Matthes, W.
1975-01-01
The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Rhoades, Ellen A.
2011-01-01
The primary purpose of a literature review is to assist readers in understanding the whole body of available research on a topic, informing readers on the strengths and weaknesses of studies within that body. It is defined by its guiding concept or topical focus: an account of what was previously published on a specific topic. This prevents…
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Regression Models for Repairable Systems
Czech Academy of Sciences Publication Activity Database
Novák, Petr
2015-01-01
Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf
Survival analysis II: Cox regression
Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.
2011-01-01
In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the
Kernel regression with functional response
Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe
2011-01-01
We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.
Meta-regression analysis of commensal and pathogenic Escherichia coli survival in soil and water.
Franz, Eelco; Schijven, Jack; de Roda Husman, Ana Maria; Blaak, Hetty
2014-06-17
The extent to which pathogenic and commensal E. coli (respectively PEC and CEC) can survive, and which factors predominantly determine the rate of decline, are crucial issues from a public health point of view. The goal of this study was to provide a quantitative summary of the variability in E. coli survival in soil and water over a broad range of individual studies and to identify the most important sources of variability. To that end, a meta-regression analysis on available literature data was conducted. The considerable variation in reported decline rates indicated that the persistence of E. coli is not easily predictable. The meta-analysis demonstrated that for soil and water, the type of experiment (laboratory or field), the matrix subtype (type of water and soil), and temperature were the main factors included in the regression analysis. A higher average decline rate in soil of PEC compared with CEC was observed. The regression models explained at best 57% of the variation in decline rate in soil and 41% of the variation in decline rate in water. This indicates that additional factors, not included in the current meta-regression analysis, are of importance but rarely reported. More complete reporting of experimental conditions may allow future inference on the global effects of these variables on the decline rate of E. coli.
DEFF Research Database (Denmark)
Dlugosz, Stephan; Mammen, Enno; Wilke, Ralf
We consider the semiparametric generalised linear regression model which has mainstream empirical models such as the (partially) linear mean regression, logistic and multinomial regression as special cases. As an extension to related literature we allow a misclassified covariate to be interacted...
Regression analysis for the social sciences
Gordon, Rachel A
2015-01-01
Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.
Failure of pan-retinal laser photocoagulation to regress ...
African Journals Online (AJOL)
Objectives: (i) To illustrate the occurrence of failure of regression of neovascularization (NV) following adequate initial and supplemental pan-retinal laser photo- coagulation (PRP) using 3 case histories (ii) To review the literature on possible aetiogenesis and further management options. Methods: The hospital records of 3 ...
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
Multivariate and semiparametric kernel regression
Härdle, Wolfgang; Müller, Marlene
1997-01-01
The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...
Regression algorithm for emotion detection
Berthelon , Franck; Sander , Peter
2013-01-01
International audience; We present here two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion~\\cite{scherer00, scherer09}. We also present a regression algorithm that determines a person's emotional feeling from sensor m...
Directional quantile regression in R
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2017-01-01
Roč. 53, č. 3 (2017), s. 480-492 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : multivariate quantile * regression quantile * halfspace depth * depth contour Subject RIV: BD - Theory of Information OBOR OECD: Applied mathematics Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0476587.pdf
Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.
Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H
2016-04-01
The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.
Polylinear regression analysis in radiochemistry
International Nuclear Information System (INIS)
Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.
1995-01-01
A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
Directory of Open Access Journals (Sweden)
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
DEFF Research Database (Denmark)
Nissen, Nina Konstantin; Holm, Lotte
2015-01-01
Improved understanding of how normal weight and moderately overweight people manage their body weight and shape could be used to inform initiatives to prevent and treat obesity. This literature review offers a thorough appraisal of existing research into perceptions and management of own body size...... among normal weight and moderately overweight people. The studies reported in the 47 publications reviewed here address various themes based on different conceptualizations. The studies point out that normal weight and moderately overweight people are much concerned about their body size, but huge...
DEFF Research Database (Denmark)
Boulus-Rødje, Nina
2012-01-01
As the utilization of various e-voting technologies has notably increased in the past few years, so has the amount of publications on experiences with these technologies. This article, will, therefore map the literature while highlighting some of the important topics discussed within the field of e...
Satellite rainfall retrieval by logistic regression
Chiu, Long S.
1986-01-01
The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.
Face Alignment via Regressing Local Binary Features.
Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian
2016-03-01
This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.
Quantum algorithm for linear regression
Wang, Guoming
2017-07-01
We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.
Interpretation of commonly used statistical regression models.
Kasza, Jessica; Wolfe, Rory
2014-01-01
A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.
Revision: Variance Inflation in Regression
Directory of Open Access Journals (Sweden)
D. R. Jensen
2013-01-01
the intercept; and (iv variance deflation may occur, where ill-conditioned data yield smaller variances than their orthogonal surrogates. Conventional VIFs have all regressors linked, or none, often untenable in practice. Beyond these, our models enable the unlinking of regressors that can be unlinked, while preserving dependence among those intrinsically linked. Moreover, known collinearity indices are extended to encompass angles between subspaces of regressors. To reaccess ill-conditioned data, we consider case studies ranging from elementary examples to data from the literature.
Dai, Wenlin; Tong, Tiejun; Zhu, Lixing
2017-01-01
Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.
Dai, Wenlin
2017-09-01
Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.
Directory of Open Access Journals (Sweden)
Ursula Miranda Bahiense de Lyra
2016-06-01
Full Text Available This research aims to highlight the importance of literature in critical thinking about the law, coupled with the search for the emergence of an autonomous political subject and as a possibility of materialization of a new right . This shall be used , bibliographic research , seeking at first discuss the historical background of the "Law and Literature Moviment " to later approach the thought of Michel Foucault , their ideas about power, the constitution subjectivity , the ethical dimension of the subject and the care of itself, the Aufklärung and its conception of this new law.
Prediction, Regression and Critical Realism
DEFF Research Database (Denmark)
Næss, Petter
2004-01-01
This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...... seen as necessary in order to identify aggregate level effects of policy measures, but are questioned by many advocates of critical realist ontology. Using research into the relationship between urban structure and travel as an example, the paper discusses relevant research methods and the kinds...
On Weighted Support Vector Regression
DEFF Research Database (Denmark)
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
Mixed kernel function support vector regression for global sensitivity analysis
Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng
2017-11-01
Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.
International Nuclear Information System (INIS)
Drury, J.S.; Michelson, D.; Ensminger, J.T.
1982-01-01
Literature was searched for methods of removing uranium from drinking water. No relevant papers were found, but approximately 1000 publications were identified in a less specific search for methods of removing uranium from water. Most of the latter publications dealt with the recovery of uranium from ores, industrial and analytical chemistry solutions, or seawater. The conditions under which these studies were performed were usually quite different from those normally occurring in municipal water treatment practice, but some potentially interesting systems of recovery were identified. A few papers addressed the problem of removing uranium from natural fresh waters and established the effectiveness of using adsorbents or coprecipitants, such as aluminum hydroxide, ferric hydroxide, activated carbon, and ion exchangers, under certain conditions. Also, many US manufacturers and users of water treatment equipment and products were contacted regarding recommended methods of removing uranium from potable water. Based on the results of these surveys, it is recommended that untreated, partially treated, and finished water samples from municipal water treatment facilities be analyzed to determine their extent of removal of uranium by presently used procedures. In addition, laboratory studies are suggested to determine what changes, if any, are needed to maximize the effectiveness of treatments that are already in use in existing water treatment plants
Geographically weighted regression and multicollinearity: dispelling the myth
Fotheringham, A. Stewart; Oshan, Taylor M.
2016-10-01
Geographically weighted regression (GWR) extends the familiar regression framework by estimating a set of parameters for any number of locations within a study area, rather than producing a single parameter estimate for each relationship specified in the model. Recent literature has suggested that GWR is highly susceptible to the effects of multicollinearity between explanatory variables and has proposed a series of local measures of multicollinearity as an indicator of potential problems. In this paper, we employ a controlled simulation to demonstrate that GWR is in fact very robust to the effects of multicollinearity. Consequently, the contention that GWR is highly susceptible to multicollinearity issues needs rethinking.
Statistical learning from a regression perspective
Berk, Richard A
2016-01-01
This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be trea...
Sirenomelia and severe caudal regression syndrome.
Seidahmed, Mohammed Z; Abdelbasit, Omer B; Alhussein, Khalid A; Miqdad, Abeer M; Khalil, Mohammed I; Salih, Mustafa A
2014-12-01
To describe cases of sirenomelia and severe caudal regression syndrome (CRS), to report the prevalence of sirenomelia, and compare our findings with the literature. Retrospective data was retrieved from the medical records of infants with the diagnosis of sirenomelia and CRS and their mothers from 1989 to 2010 (22 years) at the Security Forces Hospital, Riyadh, Saudi Arabia. A perinatologist, neonatologist, pediatric neurologist, and radiologist ascertained the diagnoses. The cases were identified as part of a study of neural tube defects during that period. A literature search was conducted using MEDLINE. During the 22-year study period, the total number of deliveries was 124,933 out of whom, 4 patients with sirenomelia, and 2 patients with severe forms of CRS were identified. All the patients with sirenomelia had single umbilical artery, and none were the infant of a diabetic mother. One patient was a twin, and another was one of triplets. The 2 patients with CRS were sisters, their mother suffered from type II diabetes mellitus and morbid obesity on insulin, and neither of them had a single umbilical artery. Other associated anomalies with sirenomelia included an absent radius, thumb, and index finger in one patient, Potter's syndrome, abnormal ribs, microphthalmia, congenital heart disease, hypoplastic lungs, and diaphragmatic hernia. The prevalence of sirenomelia (3.2 per 100,000) is high compared with the international prevalence of one per 100,000. Both cases of CRS were infants of type II diabetic mother with poor control, supporting the strong correlation of CRS and maternal diabetes.
Medizinhistorische Literatur [Medical history literature
Directory of Open Access Journals (Sweden)
Bauer, Bruno
2012-09-01
Full Text Available [english] The focus of the current issue 1-2/2012 of GMS Medizin – Bibliothek – Information is on medical history literature. In six articles special collections and recent projects of medical history libraries in Berlin, Hamburg, Heidelberg, Leipzig, Vienna and Zurich are presented. The authors in this issue are Melanie Scholz & Vera Seehausen (From Augusta to Klingsor, from Luise to Benjamin – past, present and future of the library of the Institute of the History of Medicine in Berlin, Alexandra Veith (Library of the Institute for History of Medicine and Ethics of Medicine, Heidelberg, Melanie Kintzel, Meike Knittel & Tanja Krutky (Historic collections of the Medical Library of the University of the University Medical Center Hamburg-Eppendorf and their deacidification, Dagmar Geithner (Library of the Karl Sudhoff Institute for the History of Medicine and Science, Leipzig – a Historical Review, Harald Albrecht, Bruno Bauer & Walter Mentzel (The Josephinian Library and the medical-historic stock of the University Library of the Medical University of Vienna and Monika Huber & Ursula Reis (Library of the Institute and Museum of the History of Medicine Zurich.[german] Schwerpunktthema der aktuellen Ausgabe 1-2/2012von GMS Medizin – Bibliothek – Information ist medizinhistorische Literatur. In sechs Beiträgen werden Bestände und aktuelle Projekte medizinhistorischer Bibliotheken in Berlin, Hamburg, Heidelberg, Leipzig, Wien und Zürich vorgestellt. Verfasst wurden die Beiträge der Schwerpunktausgabe von Melanie Scholz & Vera Seehausen (Von August zu Klingsor, von Luise zu Benjamin – Vergangenheit, Gegenwart und Zukunft der Bibliothek des Instituts für Geschichte der Medizin in Berlin, Melanie Kintzel, Meike Knittel & Tanja Krutky (Medizinhistorische Buchbestände am Universitätsklinikum Hamburg-Eppendorf und ihre Entsäuerung, Ara Veith (Bibliothek des Instituts für Geschichte und Ethik der Medizin in Heidelberg, Dagmar Geithner
Credit Scoring Problem Based on Regression Analysis
Khassawneh, Bashar Suhil Jad Allah
2014-01-01
ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....
Regularized Label Relaxation Linear Regression.
Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung; Fang, Bingwu
2018-04-01
Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method, which has the following notable characteristics. First, the proposed method relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible. Second, the proposed method constructs the class compactness graph based on manifold learning and uses it as the regularization item to avoid the problem of overfitting. The class compactness graph is used to ensure that the samples sharing the same labels can be kept close after they are transformed. Two different algorithms, which are, respectively, based on -norm and -norm loss functions are devised. These two algorithms have compact closed-form solutions in each iteration so that they are easily implemented. Extensive experiments show that these two algorithms outperform the state-of-the-art algorithms in terms of the classification accuracy and running time.
Directory of Open Access Journals (Sweden)
Qiutong Jin
2016-06-01
Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.
Multicultural Literature as a Classroom Tool
Osorio, Sandra L.
2018-01-01
Multicultural literature can be found all across classrooms in the United States. I argue it is more important what you do with the literature than just having it in the classroom. Multicultural literature should be seen as a tool. In this article, I will share how I used multicultural literature as a tool to (a) promote or develop an appreciation…
Warren, Mikako; Turpin, Brian K; Mark, Melissa; Smolarek, Teresa A; Li, Xia
2016-01-01
Lipoblastoma is a benign myxoid neoplasm arising in young children that typically demonstrates adipose differentiation. It is often morphologically indistinguishable from primitive myxoid mesenchymal tumor of infancy (PMMTI), which is characterized by a well-circumscribed myxoid mass with a proliferation of primitive mesenchymal cells with mild cytologic atypia. PMMTI occurs in the first year of life and is known to have locally aggressive behavior. No specific genetic rearrangements have been reported to date. In contrast, the presence of PLAG1 (Pleomorphic Adenoma Gene 1) rearrangement is diagnostic for lipoblastoma. We hereby demonstrate the combined application of multiple approaches to tackle the diagnostic challenges of a rapidly growing neck tumor in a 3-month-old female. An incisional tumor biopsy had features of an undifferentiated, myxoid mesenchymal neoplasm mimicking PMMTI. However, tumor cells showed diffuse nuclear expression by immunohistochemical (IHC) stain. Conventional cytogenetic and fluorescence in situ hybridization (FISH) analyses as well as next generation sequencing (NGS) demonstrated evidence of PLAG1 rearrangement, confirming the diagnosis of lipoblastoma. This experience warrants that undifferentiated myxoid lipoblastoma can mimic PMMTI, and the combination of cytogenetic and molecular approaches is essential to distinguish these two myxoid neoplasms. Literature on lipoblastomas with relevant molecular and cytogenetic findings is summarized. Our case is the first lipoblastoma diagnosed with a PLAG1 fusion defined by NGS technology. Copyright © 2016 Elsevier Inc. All rights reserved.
HR Department
2009-01-01
Green plates, removals and importation of personal effects Please note that, as from 1 April 2009, formalities relating to K and CD special series French vehicle plates (green plates), removals and importation of personal effects into France and Switzerland will be dealt with by GS Department (Building 73/3-014, tel. 73683/74407). Importation and purchase of tax-free vehicles in Switzerland, as well as diplomatic privileges, will continue to be dealt with by the Installation Service of HR Department (Building 33/1-011, tel. 73962). HR and GS Departments
Directory of Open Access Journals (Sweden)
José Gabriel Franco
2007-09-01
Full Text Available INTRODUCCIÓN: Muchos artículos de revistas especializadas en psiquiatría usan la regresión logística para identificar el efecto de variables independientes sobre la probabilidad de ocurrencia de un evento. Evaluaciones de artículos publicados en otras especialidades médicas han mostrado que el reporte de la regresión logística es incompleto en muchos casos, lo cual implica dificultades para su interpretación. OBJETIVO: Evaluar la calidad, en cuanto al análisis de regresión logística y a al cumplimiento de criterios de validez interna de los artículos publicados en una revista de alto factor de impacto en psiquiatría. METODOLOGÍA: Dos revisores independientes seleccionaron (búsqueda manual los artículos publicados en Archives of General Psychiatry (2002-2005 que usaran regresión logística y los evaluaron con criterios de validez interna del Journal of American Medical Association (JAMA y un instrumento diseñado para valorar la calidad de los modelos logísticos (CML. RESULTADOS: De 121 artículos evaluados, 85 (70,2% cumplían criterios de validez interna de JAMA. En relación con el instrumento CML, el criterio más reportado fue la codificación de las variables independientes (90,9%, seguido por el reporte del proceso de selección de variables independientes incluidas al inicio del estudio (87,6% y por la inclusión del RR u OR del modelo con su respectivo IC (82,6%. El criterio menos reportado fue la evaluación de la bondad del ajuste (9,1%, seguido por el reporte del proceso de ajuste del modelo (24,8%. CONCLUSIONES: Aunque la mayoría de los artículos cumplen con altos estándares editoriales, la forma en que se reporta la regresión logística debe ser mejorada.INTRODUCTION: Many articles in journals that specialize in psychiatry use logistic regression to identify the effect of independent variables on the probability of occurrence of an event. Assessments of articles published in other medical specialties have
Estonian literature / Janika Kronberg
Kronberg, Janika, 1963-
2003-01-01
Sisu: Estonian literature - born on the margins of Europe ; Baltic German literature and its impact ; Seeking the contours of a 'truly' Estonian literature ; Literature and an independent Estonia ; Estonian literature in two cultural spheres ; The fifties and sixties ; Literature and congealed time ; A bold new Estonian literature
The Regression Analysis of Individual Financial Performance: Evidence from Croatia
Bahovec, Vlasta; Barbić, Dajana; Palić, Irena
2017-01-01
Background: A large body of empirical literature indicates that gender and financial literacy are significant determinants of individual financial performance. Objectives: The purpose of this paper is to recognize the impact of the variable financial literacy and the variable gender on the variation of the financial performance using the regression analysis. Methods/Approach: The survey was conducted using the systematically chosen random sample of Croatian financial consumers. The cross sect...
Directory of Open Access Journals (Sweden)
Leonardo Alfonsi
2005-12-01
Full Text Available Few research studies have been conducted on the interpreter’s role in a Science Centre. Although the importance of this role is always stressed by museum practitioners, it seems that anecdotal evidence is the main source of information on this theme. The experience of a visitor in a Science Centre as well as in other museums has, among other things, well defined social dimensions. These dimensions are crucial in determining the quality and enjoyment of a visitor’s experience. There is evidence that suggests visitors go to a museum to meet others. Among the people that visitors meet in a Science Centre are interpreters, who help them not only to use and understand the exhibits but also to become familiar with a new environment. The following sections will illustrate what research studies say about interpreters, considering their twofold relation with visitors and exhibit developers.
Hartmann, Frank G.H.; Moers, Frank
1999-01-01
In the contingency literature on the behavioral and organizational effects of budgeting, use of the Moderated Regression Analysis (MRA) technique is prevalent. This technique is used to test contingency hypotheses that predict interaction effects between budgetary and contextual variables. This
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor
2012-06-29
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor; Midthune, Douglas; Freedman, Laurence S.; Carroll, Raymond J.
2012-01-01
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
Moderation analysis using a two-level regression model.
Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott
2014-10-01
Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.
A flexible fuzzy regression algorithm for forecasting oil consumption estimation
International Nuclear Information System (INIS)
Azadeh, A.; Khakestani, M.; Saberi, M.
2009-01-01
Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions.
Impact of regression methods on improved effects of soil structure on soil water retention estimates
Nguyen, Phuong Minh; De Pue, Jan; Le, Khoa Van; Cornelis, Wim
2015-06-01
Increasing the accuracy of pedotransfer functions (PTFs), an indirect method for predicting non-readily available soil features such as soil water retention characteristics (SWRC), is of crucial importance for large scale agro-hydrological modeling. Adding significant predictors (i.e., soil structure), and implementing more flexible regression algorithms are among the main strategies of PTFs improvement. The aim of this study was to investigate whether the improved effect of categorical soil structure information on estimating soil-water content at various matric potentials, which has been reported in literature, could be enduringly captured by regression techniques other than the usually applied linear regression. Two data mining techniques, i.e., Support Vector Machines (SVM), and k-Nearest Neighbors (kNN), which have been recently introduced as promising tools for PTF development, were utilized to test if the incorporation of soil structure will improve PTF's accuracy under a context of rather limited training data. The results show that incorporating descriptive soil structure information, i.e., massive, structured and structureless, as grouping criterion can improve the accuracy of PTFs derived by SVM approach in the range of matric potential of -6 to -33 kPa (average RMSE decreased up to 0.005 m3 m-3 after grouping, depending on matric potentials). The improvement was primarily attributed to the outperformance of SVM-PTFs calibrated on structureless soils. No improvement was obtained with kNN technique, at least not in our study in which the data set became limited in size after grouping. Since there is an impact of regression techniques on the improved effect of incorporating qualitative soil structure information, selecting a proper technique will help to maximize the combined influence of flexible regression algorithms and soil structure information on PTF accuracy.
Critical appraisal of published literature
Umesh, Goneppanavar; Karippacheril, John George; Magazine, Rahul
2016-01-01
With a large output of medical literature coming out every year, it is impossible for readers to read every article. Critical appraisal of scientific literature is an important skill to be mastered not only by academic medical professionals but also by those involved in clinical practice. Before incorporating changes into the management of their patients, a thorough evaluation of the current or published literature is an important step in clinical practice. It is necessary for assessing the published literature for its scientific validity and generalizability to the specific patient community and reader's work environment. Simple steps have been provided by Consolidated Standard for Reporting Trial statements, Scottish Intercollegiate Guidelines Network and several other resources which if implemented may help the reader to avoid reading flawed literature and prevent the incorporation of biased or untrustworthy information into our practice. PMID:27729695
Critical appraisal of published literature
Directory of Open Access Journals (Sweden)
Goneppanavar Umesh
2016-01-01
Full Text Available With a large output of medical literature coming out every year, it is impossible for readers to read every article. Critical appraisal of scientific literature is an important skill to be mastered not only by academic medical professionals but also by those involved in clinical practice. Before incorporating changes into the management of their patients, a thorough evaluation of the current or published literature is an important step in clinical practice. It is necessary for assessing the published literature for its scientific validity and generalizability to the specific patient community and reader′s work environment. Simple steps have been provided by Consolidated Standard for Reporting Trial statements, Scottish Intercollegiate Guidelines Network and several other resources which if implemented may help the reader to avoid reading flawed literature and prevent the incorporation of biased or untrustworthy information into our practice.
Literature promotion in Public Libraries
DEFF Research Database (Denmark)
Kann-Christensen, Nanna; Balling, Gitte
2011-01-01
This article discusses a model that can be used in order to analyse notions on literature promotion in public libraries. The model integrates different issues which interact with how literature promotion is understood and thought of in public libraries. Besides cultural policy we regard the logics...... of new public management (NPM) and professional logics in the field of public libraries. Cultural policy along with the identification of underlying logics present among politicians, government officials, managers and librarians/promoters of literature, play an important part in creating an understanding...... of literature promotion in Danish libraries. Thus the basic premise for the development of the model is that cultural policy (Policy) has an important influence on notions on literature promotion and other activities in public libraries, but that cultural policy must be seen in some kind of interaction...
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...
Semiparametric regression during 2003–2007
Ruppert, David; Wand, M.P.; Carroll, Raymond J.
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Gaussian process regression analysis for functional data
Shi, Jian Qing
2011-01-01
Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime
Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression
Beckstead, Jason W.
2012-01-01
The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…
ANALYSIS OF THE FINANCIAL PERFORMANCES OF THE FIRM, BY USING THE MULTIPLE REGRESSION MODEL
Directory of Open Access Journals (Sweden)
Constantin Anghelache
2011-11-01
Full Text Available The information achieved through the use of simple linear regression are not always enough to characterize the evolution of an economic phenomenon and, furthermore, to identify its possible future evolution. To remedy these drawbacks, the special literature includes multiple regression models, in which the evolution of the dependant variable is defined depending on two or more factorial variables.
DEFF Research Database (Denmark)
Bini, L. M.; Diniz-Filho, J. A. F.; Rangel, T. F. L. V. B.
2009-01-01
A major focus of geographical ecology and macroecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regression, because the relative importance of explanatory variables, as measured by regress...
Regression Analysis by Example. 5th Edition
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
A Seemingly Unrelated Poisson Regression Model
King, Gary
1989-01-01
This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.
International Nuclear Information System (INIS)
Mizumachi, Wataru; Kobayashi, Masahide
2008-01-01
Conventionally, the design of a nuclear reactor has been performed from a viewpoint of a safety function and the importance on earthquake-proof on the basis of not giving off the mainly included radioactivity outside. In this Niigataken-Chuetsuoki earthquake, there is almost no damage to the system, components and structure on safe also in the earthquake beyond assumption, and the validity of the design was checked. But, the situation peculiar to a big earthquake was also generated. The emergency plan room which should serve as a connection center with the exterior was not able to open a door and use at the beginning. Fire-extinguishing system piping fractured and self-defense fire fighting was not made. And so on. Discussion from the following three viewpoints was performed. 1st: The importance from a viewpoint which should maintain a function also with the disaster in case of an earthquake like an emergency plan room etc. 2nd: In the earthquake, since the safe system and un-safe system was influenced, the importance from a viewpoint which may have influence safely inquired when the un-safe system broke down. 3rd: Although it was not directly related safely, discussion from a viewpoint which influences fear of insecurity, such as taking out smoke, for example, was performed (author)
Cointegrating MiDaS Regressions and a MiDaS Test
J. Isaac Miller
2011-01-01
This paper introduces cointegrating mixed data sampling (CoMiDaS) regressions, generalizing nonlinear MiDaS regressions in the extant literature. Under a linear mixed-frequency data-generating process, MiDaS regressions provide a parsimoniously parameterized nonlinear alternative when the linear forecasting model is over-parameterized and may be infeasible. In spite of potential correlation of the error term both serially and with the regressors, I find that nonlinear least squares consistent...
How important is importance for prospective memory? A review
Walter, Stefan; Meier, Beat
2014-01-01
Forgetting to carry out an intention as planned can have serious consequences in everyday life. People sometimes even forget intentions that they consider as very important. Here, we review the literature on the impact of importance on prospective memory performance. We highlight different methods used to manipulate the importance of a prospective memory task such as providing rewards, importance relative to other ongoing activities, absolute importance, and providing social motives. Moreover...
Regression with Sparse Approximations of Data
DEFF Research Database (Denmark)
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...
Spontaneous regression of a congenital melanocytic nevus
Directory of Open Access Journals (Sweden)
Amiya Kumar Nath
2011-01-01
Full Text Available Congenital melanocytic nevus (CMN may rarely regress which may also be associated with a halo or vitiligo. We describe a 10-year-old girl who presented with CMN on the left leg since birth, which recently started to regress spontaneously with associated depigmentation in the lesion and at a distant site. Dermoscopy performed at different sites of the regressing lesion demonstrated loss of epidermal pigments first followed by loss of dermal pigments. Histopathology and Masson-Fontana stain demonstrated lymphocytic infiltration and loss of pigment production in the regressing area. Immunohistochemistry staining (S100 and HMB-45, however, showed that nevus cells were present in the regressing areas.
Directory of Open Access Journals (Sweden)
Kenneth David Strang
2009-03-01
Full Text Available This paper discusses how a seldom-used statistical procedure, recursive regression (RR, can numerically and graphically illustrate data-driven nonlinear relationships and interaction of variables. This routine falls into the family of exploratory techniques, yet a few interesting features make it a valuable compliment to factor analysis and multiple linear regression for method triangulation. By comparison, nonlinear cluster analysis also generates graphical dendrograms to visually depict relationships, but RR (as implemented here uses multiple combinations of nominal and interval predictors regressed on a categorical or ratio dependent variable. In similar fashion, multidimensional scaling, multiple discriminant analysis and conjoint analysis are constrained at best to predicting an ordinal dependent variable (as currently implemented in popular software. A flexible capability of RR (again as implemented here is the transformation of factor data (for substituting codes. One powerful RR feature is the ability to treat missing data as a theoretically important predictor value (useful for survey questions that respondents do not wish to answer. For practitioners, the paper summarizes how this technique fits within the generally-accepted statistical methods. Popular software such as SPSS, SAS or LISREL can be used, while sample data can be imported in common formats including ASCII text, comma delimited, Excel XLS, and SPSS SAV. A tutorial approach is applied here using RR in LISREL. The tutorial leverages a partial sample from a study that used recursive regression to predict grades from international student learning styles. Some tutorial portions are technical, to improve the ambiguous RR literature.
Background stratified Poisson regression analysis of cohort data.
Richardson, David B; Langholz, Bryan
2012-03-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.
Independent contrasts and PGLS regression estimators are equivalent.
Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary
2012-05-01
We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.
Background stratified Poisson regression analysis of cohort data
International Nuclear Information System (INIS)
Richardson, David B.; Langholz, Bryan
2012-01-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. (orig.)
International Nuclear Information System (INIS)
Yamaguchi, N.D.
2001-01-01
This article focuses on bitumens and bitumen products from Canadian oil sands and explores how they will affect the Canadian oil industry and the US refining industry. The falling production of crude, the growing demand for it, and the stagnating refining capacity in the US are reported, and Canadian and Mexican exports to the US, the definition of bitumens and bitumen quality, and the position of Canada as world leader in bitumen resources are considered. Bitumen production techniques, sales of bitumens and synthetic crude, the production outlook, and the quality and refining of bitumen and synthetic crude are examined. Plots illustrating North American crude refining capacity, production and demand for 1980-2000; US crude imports from Canada and Mexico (1981-2000), world proven oil reserves (2001), world bitumen resources, and Canadian oil production (1998-2000) are provided. Details of the composition of crudes and bitumens, and recent synthetic crude production are tabulated
Enders, Felicity
2013-12-01
Although regression is widely used for reading and publishing in the medical literature, no instruments were previously available to assess students' understanding. The goal of this study was to design and assess such an instrument for graduate students in Clinical and Translational Science and Public Health. A 27-item REsearch on Global Regression Expectations in StatisticS (REGRESS) quiz was developed through an iterative process. Consenting students taking a course on linear regression in a Clinical and Translational Science program completed the quiz pre- and postcourse. Student results were compared to practicing statisticians with a master's or doctoral degree in statistics or a closely related field. Fifty-two students responded precourse, 59 postcourse , and 22 practicing statisticians completed the quiz. The mean (SD) score was 9.3 (4.3) for students precourse and 19.0 (3.5) postcourse (P REGRESS quiz was internally reliable (Cronbach's alpha 0.89). The initial validation is quite promising with statistically significant and meaningful differences across time and study populations. Further work is needed to validate the quiz across multiple institutions. © 2013 Wiley Periodicals, Inc.
Collaborative regression-based anatomical landmark detection
International Nuclear Information System (INIS)
Gao, Yaozong; Shen, Dinggang
2015-01-01
Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of ‘difficult-to-detect’ landmarks by using spatial guidance from ‘easy-to-detect’ landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head and neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods. (paper)
Structured Literature Review of digital disruption literature
DEFF Research Database (Denmark)
Vesti, Helle; Rosenstand, Claus Andreas Foss; Gertsen, Frank
2018-01-01
Digital disruption is a term/phenomenon frequently appearing in innovation management literature. However, no academic consensus exists as to what it entails; conceptual nor theoretical. We use the SLR-method (Structured Literature Review) to investigate digital disruption literature. A SLR......-study conducted in 2017 revealed some useful information on how disruption and digital disruption literature has developed over a specific period. However, this study was less representative of papers addressing digital disruption; which is the in-depth subject of this paper. To accommodate this, we intend...... to conduct a similar SLR-study assembling a body literature having digital disruption as the only common denominator...
Applied regression analysis a research tool
Pantula, Sastry; Dickey, David
1998-01-01
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...
Multivariate Frequency-Severity Regression Models in Insurance
Directory of Open Access Journals (Sweden)
Edward W. Frees
2016-02-01
Full Text Available In insurance and related industries including healthcare, it is common to have several outcome measures that the analyst wishes to understand using explanatory variables. For example, in automobile insurance, an accident may result in payments for damage to one’s own vehicle, damage to another party’s vehicle, or personal injury. It is also common to be interested in the frequency of accidents in addition to the severity of the claim amounts. This paper synthesizes and extends the literature on multivariate frequency-severity regression modeling with a focus on insurance industry applications. Regression models for understanding the distribution of each outcome continue to be developed yet there now exists a solid body of literature for the marginal outcomes. This paper contributes to this body of literature by focusing on the use of a copula for modeling the dependence among these outcomes; a major advantage of this tool is that it preserves the body of work established for marginal models. We illustrate this approach using data from the Wisconsin Local Government Property Insurance Fund. This fund offers insurance protection for (i property; (ii motor vehicle; and (iii contractors’ equipment claims. In addition to several claim types and frequency-severity components, outcomes can be further categorized by time and space, requiring complex dependency modeling. We find significant dependencies for these data; specifically, we find that dependencies among lines are stronger than the dependencies between the frequency and average severity within each line.
Regression models of reactor diagnostic signals
International Nuclear Information System (INIS)
Vavrin, J.
1989-01-01
The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
Multivariate Regression Analysis and Slaughter Livestock,
AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY
Candida albicans importance to denture wearers. A literature review.
Gleiznys, Alvydas; Zdanavičienė, Eglė; Žilinskas, Juozas
2015-01-01
Opportunistic oral fungal infections have spred, especially in denture wearers. Denture stomatitis is a common inflammatory reaction, multifactorial etiology, which is usually associated with Candida species, particularly Candida albicans, due to its high virulence, ability to adhere and form biofilms on oral cavity tissues and denture surfaces. This article highlights the pathogenesis, clinical presentation, and management strategies of Candida-associated denture stomatitis commonly encountered in dental practice.
The most important persons in the management science: literature review
Borshch, Viktoriya I.; Huz, V. V.
2018-01-01
This article provides a deep research about the most known gurus of management: Henri Fayol, who invented the basic 14 Principles of Management, Michael Porter and his competition concept, at last Peter Drucker, the inventor of MBO system. Nowadays society is still using Fayol’s Porter’s and Drucker’s discoveries.Research subject of this article is knowledge from the management founders. The objective of the article is to determine the main issues, which they tried to explain and solve. There...
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
Ernst, Anja F; Albers, Casper J
2017-01-01
Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.
On European Religious Literature
Institute of Scientific and Technical Information of China (English)
张丽娟
2016-01-01
Since ancient time,literature has being a hot topic that scholars concern.Latin religious literature is the mainstream of medieval literature.This paper analyzes medieval literature from three aspects which are the religious cultural background,main characteristics and achievements.What’s more,the thesis summarizes its influence to literature afterwards,and provides suggestion to the contemporary literature in China.
Leadership and regressive group processes: a pilot study.
Rudden, Marie G; Twemlow, Stuart; Ackerman, Steven
2008-10-01
Various perspectives on leadership within the psychoanalytic, organizational and sociobiological literature are reviewed, with particular attention to research studies in these areas. Hypotheses are offered about what makes an effective leader: her ability to structure tasks well in order to avoid destructive regressions, to make constructive use of the omnipresent regressive energies in group life, and to redirect regressions when they occur. Systematic qualitative observations of three videotaped sessions each from N = 18 medical staff work groups at an urban medical center are discussed, as is the utility of a scale, the Leadership and Group Regressions Scale (LGRS), that attempts to operationalize the hypotheses. Analyzing the tapes qualitatively, it was noteworthy that at times (in N = 6 groups), the nominal leader of the group did not prove to be the actual, working leader. Quantitatively, a significant correlation was seen between leaders' LGRS scores and the group's satisfactory completion of their quantitative goals (p = 0.007) and ability to sustain the goals (p = 0.04), when the score of the person who met criteria for group leadership was used.
Testing the Perturbation Sensitivity of Abortion-Crime Regressions
Directory of Open Access Journals (Sweden)
Michał Brzeziński
2012-06-01
Full Text Available The hypothesis that the legalisation of abortion contributed significantly to the reduction of crime in the United States in 1990s is one of the most prominent ideas from the recent “economics-made-fun” movement sparked by the book Freakonomics. This paper expands on the existing literature about the computational stability of abortion-crime regressions by testing the sensitivity of coefficients’ estimates to small amounts of data perturbation. In contrast to previous studies, we use a new data set on crime correlates for each of the US states, the original model specifica-tion and estimation methodology, and an improved data perturbation algorithm. We find that the coefficients’ estimates in abortion-crime regressions are not computationally stable and, therefore, are unreliable.
bayesQR: A Bayesian Approach to Quantile Regression
Directory of Open Access Journals (Sweden)
Dries F. Benoit
2017-01-01
Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.
Real estate value prediction using multivariate regression models
Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav
2017-11-01
The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.
Logistic regression for risk factor modelling in stuttering research.
Reed, Phil; Wu, Yaqionq
2013-06-01
To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.
DNBR Prediction Using a Support Vector Regression
International Nuclear Information System (INIS)
Yang, Heon Young; Na, Man Gyun
2008-01-01
PWRs (Pressurized Water Reactors) generally operate in the nucleate boiling state. However, the conversion of nucleate boiling into film boiling with conspicuously reduced heat transfer induces a boiling crisis that may cause the fuel clad melting in the long run. This type of boiling crisis is called Departure from Nucleate Boiling (DNB) phenomena. Because the prediction of minimum DNBR in a reactor core is very important to prevent the boiling crisis such as clad melting, a lot of research has been conducted to predict DNBR values. The object of this research is to predict minimum DNBR applying support vector regression (SVR) by using the measured signals of a reactor coolant system (RCS). The SVR has extensively and successfully been applied to nonlinear function approximation like the proposed problem for estimating DNBR values that will be a function of various input variables such as reactor power, reactor pressure, core mass flowrate, control rod positions and so on. The minimum DNBR in a reactor core is predicted using these various operating condition data as the inputs to the SVR. The minimum DBNR values predicted by the SVR confirm its correctness compared with COLSS values
Exact Rational Expectations, Cointegration, and Reduced Rank Regression
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
We interpret the linear relations from exact rational expectations models as restrictions on the parameters of the statistical model called the cointegrated vector autoregressive model for non-stationary variables. We then show how reduced rank regression, Anderson (1951), plays an important role...
Exact rational expectations, cointegration, and reduced rank regression
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
We interpret the linear relations from exact rational expectations models as restrictions on the parameters of the statistical model called the cointegrated vector autoregressive model for non-stationary variables. We then show how reduced rank regression, Anderson (1951), plays an important role...
Exact rational expectations, cointegration, and reduced rank regression
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
2008-01-01
We interpret the linear relations from exact rational expectations models as restrictions on the parameters of the statistical model called the cointegrated vector autoregressive model for non-stationary variables. We then show how reduced rank regression, Anderson (1951), plays an important role...
Wind speed prediction using statistical regression and neural network
Indian Academy of Sciences (India)
Prediction of wind speed in the atmospheric boundary layer is important for wind energy assess- ment,satellite launching and aviation,etc.There are a few techniques available for wind speed prediction,which require a minimum number of input parameters.Four different statistical techniques,viz.,curve ﬁtting,Auto Regressive ...
RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,
This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)
A Simulation Investigation of Principal Component Regression.
Allen, David E.
Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…
Hierarchical regression analysis in structural Equation Modeling
de Jong, P.F.
1999-01-01
In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main
Categorical regression dose-response modeling
The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...
Stepwise versus Hierarchical Regression: Pros and Cons
Lewis, Mitzi
2007-01-01
Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…
Repeated Results Analysis for Middleware Regression Benchmarking
Czech Academy of Sciences Publication Activity Database
Bulej, Lubomír; Kalibera, T.; Tůma, P.
2005-01-01
Roč. 60, - (2005), s. 345-358 ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005
Principles of Quantile Regression and an Application
Chen, Fang; Chalhoub-Deville, Micheline
2014-01-01
Newer statistical procedures are typically introduced to help address the limitations of those already in practice or to deal with emerging research needs. Quantile regression (QR) is introduced in this paper as a relatively new methodology, which is intended to overcome some of the limitations of least squares mean regression (LMR). QR is more…
ON REGRESSION REPRESENTATIONS OF STOCHASTIC-PROCESSES
RUSCHENDORF, L; DEVALK, [No Value
We construct a.s. nonlinear regression representations of general stochastic processes (X(n))n is-an-element-of N. As a consequence we obtain in particular special regression representations of Markov chains and of certain m-dependent sequences. For m-dependent sequences we obtain a constructive
Li, Jiangtong; Luo, Yongdao; Dai, Honglin
2018-01-01
Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.
Regression of environmental noise in LIGO data
International Nuclear Information System (INIS)
Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G
2015-01-01
We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)
Should metacognition be measured by logistic regression?
Rausch, Manuel; Zehetleitner, Michael
2017-03-01
Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Janssen, I.; Stebbings, J.H.
1990-01-01
In environmental epidemiology, trace and toxic substance concentrations frequently have very highly skewed distributions ranging over one or more orders of magnitude, and prediction by conventional regression is often poor. Classification and Regression Tree Analysis (CART) is an alternative in such contexts. To compare the techniques, two Pennsylvania data sets and three independent variables are used: house radon progeny (RnD) and gamma levels as predicted by construction characteristics in 1330 houses; and ∼200 house radon (Rn) measurements as predicted by topographic parameters. CART may identify structural variables of interest not identified by conventional regression, and vice versa, but in general the regression models are similar. CART has major advantages in dealing with other common characteristics of environmental data sets, such as missing values, continuous variables requiring transformations, and large sets of potential independent variables. CART is most useful in the identification and screening of independent variables, greatly reducing the need for cross-tabulations and nested breakdown analyses. There is no need to discard cases with missing values for the independent variables because surrogate variables are intrinsic to CART. The tree-structured approach is also independent of the scale on which the independent variables are measured, so that transformations are unnecessary. CART identifies important interactions as well as main effects. The major advantages of CART appear to be in exploring data. Once the important variables are identified, conventional regressions seem to lead to results similar but more interpretable by most audiences. 12 refs., 8 figs., 10 tabs
Bengal Literature and History.
Dimock, Edward C., Jr., Ed.
The unifying theme of the papers in this book is the use of creative literature as source material for the study of cultural history. Titles and authors of the papers are: "Encounter and Growth in Bengali Literature, A Survey of Medieval Bengali Literature" by T.W. Clark; "The Hindu Chiefdom in Middle Bengali Literature" by…
Institute of Scientific and Technical Information of China (English)
田琳
2016-01-01
In both China and the West, mathematics is closely connected with literature. The maths thought implied in Chinese and western literature is worth our study, and the maths thought in the field of literature is also appear in aesthetics and philoso-phy, so literature, mathematics, aesthetics and philosophy become a network of interconnected.
Determinants of Non-Performing Assets in India - Panel Regression
Directory of Open Access Journals (Sweden)
Saikat Ghosh Roy
2014-12-01
Full Text Available It is well known that level of banks‟ credit plays an important role in economic developments. Indian banking sector has played a seminal role in supporting economic growth in India. Recently, Indian banks are experiencing consistent increase in non-performing assets (NPA. In this perspective, this paper investigates the trends in NPA in Indian banks and its determinants. The panel regressions, fixed effect allows evaluating the impact of selected macroeconomic variables on the NPA. The Panel regression result indicates that the GDP growth, change in exchange rate and global volatility have major effects on the NPA level of Indian banking sector.
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
FBH1 Catalyzes Regression of Stalled Replication Forks
Directory of Open Access Journals (Sweden)
Kasper Fugger
2015-03-01
Full Text Available DNA replication fork perturbation is a major challenge to the maintenance of genome integrity. It has been suggested that processing of stalled forks might involve fork regression, in which the fork reverses and the two nascent DNA strands anneal. Here, we show that FBH1 catalyzes regression of a model replication fork in vitro and promotes fork regression in vivo in response to replication perturbation. Cells respond to fork stalling by activating checkpoint responses requiring signaling through stress-activated protein kinases. Importantly, we show that FBH1, through its helicase activity, is required for early phosphorylation of ATM substrates such as CHK2 and CtIP as well as hyperphosphorylation of RPA. These phosphorylations occur prior to apparent DNA double-strand break formation. Furthermore, FBH1-dependent signaling promotes checkpoint control and preserves genome integrity. We propose a model whereby FBH1 promotes early checkpoint signaling by remodeling of stalled DNA replication forks.
Approaches to Low Fuel Regression Rate in Hybrid Rocket Engines
Directory of Open Access Journals (Sweden)
Dario Pastrone
2012-01-01
Full Text Available Hybrid rocket engines are promising propulsion systems which present appealing features such as safety, low cost, and environmental friendliness. On the other hand, certain issues hamper the development hoped for. The present paper discusses approaches addressing improvements to one of the most important among these issues: low fuel regression rate. To highlight the consequence of such an issue and to better understand the concepts proposed, fundamentals are summarized. Two approaches are presented (multiport grain and high mixture ratio which aim at reducing negative effects without enhancing regression rate. Furthermore, fuel material changes and nonconventional geometries of grain and/or injector are presented as methods to increase fuel regression rate. Although most of these approaches are still at the laboratory or concept scale, many of them are promising.
Detection of Outliers in Regression Model for Medical Data
Directory of Open Access Journals (Sweden)
Stephen Raj S
2017-07-01
Full Text Available In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The detection of outliers and influential points is an important step of the regression analysis. Outlier detection methods have been used to detect and remove anomalous values from data. In this paper, we detect the presence of outliers in simple linear regression models for medical data set. Chatterjee and Hadi mentioned that the ordinary residuals are not appropriate for diagnostic purposes; a transformed version of them is preferable. First, we investigate the presence of outliers based on existing procedures of residuals and standardized residuals. Next, we have used the new approach of standardized scores for detecting outliers without the use of predicted values. The performance of the new approach was verified with the real-life data.
Two SPSS programs for interpreting multiple regression results.
Lorenzo-Seva, Urbano; Ferrando, Pere J; Chico, Eliseo
2010-02-01
When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http://brm.psychonomic-journals.org/content/supplemental.
Vectors, a tool in statistical regression theory
Corsten, L.C.A.
1958-01-01
Using linear algebra this thesis developed linear regression analysis including analysis of variance, covariance analysis, special experimental designs, linear and fertility adjustments, analysis of experiments at different places and times. The determination of the orthogonal projection, yielding
Genetics Home Reference: caudal regression syndrome
... umbilical artery: Further support for a caudal regression-sirenomelia spectrum. Am J Med Genet A. 2007 Dec ... AK, Dickinson JE, Bower C. Caudal dysgenesis and sirenomelia-single centre experience suggests common pathogenic basis. Am ...
Dynamic travel time estimation using regression trees.
2008-10-01
This report presents a methodology for travel time estimation by using regression trees. The dissemination of travel time information has become crucial for effective traffic management, especially under congested road conditions. In the absence of c...
Two Paradoxes in Linear Regression Analysis
FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong
2016-01-01
Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214
Discriminative Elastic-Net Regularized Linear Regression.
Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen
2017-03-01
In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.
Fuzzy multiple linear regression: A computational approach
Juang, C. H.; Huang, X. H.; Fleming, J. W.
1992-01-01
This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.
Computing multiple-output regression quantile regions
Czech Academy of Sciences Publication Activity Database
Paindaveine, D.; Šiman, Miroslav
2012-01-01
Roč. 56, č. 4 (2012), s. 840-853 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M06047 Institutional research plan: CEZ:AV0Z10750506 Keywords : halfspace depth * multiple-output regression * parametric linear programming * quantile regression Subject RIV: BA - General Mathematics Impact factor: 1.304, year: 2012 http://library.utia.cas.cz/separaty/2012/SI/siman-0376413.pdf
There is No Quantum Regression Theorem
International Nuclear Information System (INIS)
Ford, G.W.; OConnell, R.F.
1996-01-01
The Onsager regression hypothesis states that the regression of fluctuations is governed by macroscopic equations describing the approach to equilibrium. It is here asserted that this hypothesis fails in the quantum case. This is shown first by explicit calculation for the example of quantum Brownian motion of an oscillator and then in general from the fluctuation-dissipation theorem. It is asserted that the correct generalization of the Onsager hypothesis is the fluctuation-dissipation theorem. copyright 1996 The American Physical Society
Caudal regression syndrome : a case report
International Nuclear Information System (INIS)
Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun
1998-01-01
Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging
Caudal regression syndrome : a case report
Energy Technology Data Exchange (ETDEWEB)
Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun [Chungang Gil Hospital, Incheon (Korea, Republic of)
1998-07-01
Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging.
Spontaneous regression of metastatic Merkel cell carcinoma.
LENUS (Irish Health Repository)
Hassan, S J
2010-01-01
Merkel cell carcinoma is a rare aggressive neuroendocrine carcinoma of the skin predominantly affecting elderly Caucasians. It has a high rate of local recurrence and regional lymph node metastases. It is associated with a poor prognosis. Complete spontaneous regression of Merkel cell carcinoma has been reported but is a poorly understood phenomenon. Here we present a case of complete spontaneous regression of metastatic Merkel cell carcinoma demonstrating a markedly different pattern of events from those previously published.
Forecasting exchange rates: a robust regression approach
Preminger, Arie; Franck, Raphael
2005-01-01
The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach, based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by outliers. A robust linear autoregressive (RAR) and a robust neural network (RNN) models are estimated to study the predictabil...
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.; Carroll, R.J.; Wand, M.P.
2010-01-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...... in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest...
Spontaneous regression of a large hepatocellular carcinoma: case report
Directory of Open Access Journals (Sweden)
Alqutub, Adel
2011-01-01
Full Text Available The prognosis of untreated advanced hepatocellular carcinoma (HCC is grim with a median survival of less than 6 months. Spontaneous regression of HCC has been defined as the disappearance of the hepatic lesions in the absence of any specific therapy. The spontaneous regression of a very large HCC is very rare and limited data is available in the English literature. We describe spontaneous regression of hepatocellular carcinoma in a 65-year-old male who presented to our clinic with vague abdominal pain and weight loss of two months duration. He was found to have multiple hepatic lesions with elevation of serum alpha-fetoprotein (AFP level to 6,500 µg/L (normal <20 µg/L. Computed tomography revealed advanced HCC replacing almost 80% of the right hepatic lobe. Without any intervention the patient showed gradual improvement over a period of few months. Follow-up CT scan revealed disappearance of hepatic lesions with progressive decline of AFP levels to normal. Various mechanisms have been postulated to explain this rare phenomenon, but the exact mechanism remains a mystery.
ADORNO ON LITERATURE AND EDUCATION
Directory of Open Access Journals (Sweden)
Filipe Ceppas
2015-08-01
Full Text Available This paper presents correlated themes of Adorno’s theory of literature and Education. It draws attention to ‘postmodern aspects’ of these themes and indicates its importance to overcome the limits of a plaintive reading of Adorno’s ideas.
Grey Literature and the Internet
Hartman, Karen A.
2006-01-01
Accreditation standards for professional schools offering social work degrees mandate curriculum content that provides students with skills to analyze, formulate, and influence social policies. An important source of analytical thinking about social policy is the "grey" literature issued by public policy organizations, think tanks,…
On Bayesian shared component disease mapping and ecological regression with errors in covariates.
MacNab, Ying C
2010-05-20
Recent literature on Bayesian disease mapping presents shared component models (SCMs) for joint spatial modeling of two or more diseases with common risk factors. In this study, Bayesian hierarchical formulations of shared component disease mapping and ecological models are explored and developed in the context of ecological regression, taking into consideration errors in covariates. A review of multivariate disease mapping models (MultiVMs) such as the multivariate conditional autoregressive models that are also part of the more recent Bayesian disease mapping literature is presented. Some insights into the connections and distinctions between the SCM and MultiVM procedures are communicated. Important issues surrounding (appropriate) formulation of shared- and disease-specific components, consideration/choice of spatial or non-spatial random effects priors, and identification of model parameters in SCMs are explored and discussed in the context of spatial and ecological analysis of small area multivariate disease or health outcome rates and associated ecological risk factors. The methods are illustrated through an in-depth analysis of four-variate road traffic accident injury (RTAI) data: gender-specific fatal and non-fatal RTAI rates in 84 local health areas in British Columbia (Canada). Fully Bayesian inference via Markov chain Monte Carlo simulations is presented. Copyright 2010 John Wiley & Sons, Ltd.
Leite, Fábio R M; Nascimento, Gustavo G; Demarco, Flávio F; Gomes, Brenda P F A; Pucci, Cesar R; Martinho, Frederico C
2015-05-01
This systematic review and meta-regression analysis aimed to calculate a combined prevalence estimate and evaluate the prevalence of different Treponema species in primary and secondary endodontic infections, including symptomatic and asymptomatic cases. The MEDLINE/PubMed, Embase, Scielo, Web of Knowledge, and Scopus databases were searched without starting date restriction up to and including March 2014. Only reports in English were included. The selected literature was reviewed by 2 authors and classified as suitable or not to be included in this review. Lists were compared, and, in case of disagreements, decisions were made after a discussion based on inclusion and exclusion criteria. A pooled prevalence of Treponema species in endodontic infections was estimated. Additionally, a meta-regression analysis was performed. Among the 265 articles identified in the initial search, only 51 were included in the final analysis. The studies were classified into 2 different groups according to the type of endodontic infection and whether it was an exclusively primary/secondary study (n = 36) or a primary/secondary comparison (n = 15). The pooled prevalence of Treponema species was 41.5% (95% confidence interval, 35.9-47.0). In the multivariate model of meta-regression analysis, primary endodontic infections (P apical abscess, symptomatic apical periodontitis (P < .001), and concomitant presence of 2 or more species (P = .028) explained the heterogeneity regarding the prevalence rates of Treponema species. Our findings suggest that Treponema species are important pathogens involved in endodontic infections, particularly in cases of primary and acute infections. Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.
The crux of the method: assumptions in ordinary least squares and logistic regression.
Long, Rebecca G
2008-10-01
Logistic regression has increasingly become the tool of choice when analyzing data with a binary dependent variable. While resources relating to the technique are widely available, clear discussions of why logistic regression should be used in place of ordinary least squares regression are difficult to find. The current paper compares and contrasts the assumptions of ordinary least squares with those of logistic regression and explains why logistic regression's looser assumptions make it adept at handling violations of the more important assumptions in ordinary least squares.
Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro
2012-11-01
Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright
Regression analysis using dependent Polya trees.
Schörgendorfer, Angela; Branscum, Adam J
2013-11-30
Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.
World Literature - World Culture
DEFF Research Database (Denmark)
Offering their own twenty-first-century perspectives - across generations, nationalities and disciplines -, the contributors to this anthology explore the idea of world literature for what it may add of new connections and itineraries to the study of literature and culture today. Covering a vast...... historical material these essays, by a diverse group of scholars, examine the pioneers of world literature and the roles played by translation, migration and literary institutions in the circulation and reception of both national and cosmopolitan literatures....
DEFF Research Database (Denmark)
Bjerre, Thomas Ærvold
2017-01-01
Provides an outline of Southern Gothic Literature, offers an argument about its history and shape, and discusses the scholarly literature surrounding Southern Gothic. Oxford Research Encyclopedia is an online peer-reviewed encyclopedia for researchers, teachers, and students interested in all...... facets of the study of literature...
On Solving Lq-Penalized Regressions
Directory of Open Access Journals (Sweden)
Tracy Zhou Wu
2007-01-01
Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.
Refractive regression after laser in situ keratomileusis.
Yan, Mabel K; Chang, John Sm; Chan, Tommy Cy
2018-04-26
Uncorrected refractive errors are a leading cause of visual impairment across the world. In today's society, laser in situ keratomileusis (LASIK) has become the most commonly performed surgical procedure to correct refractive errors. However, regression of the initially achieved refractive correction has been a widely observed phenomenon following LASIK since its inception more than two decades ago. Despite technological advances in laser refractive surgery and various proposed management strategies, post-LASIK regression is still frequently observed and has significant implications for the long-term visual performance and quality of life of patients. This review explores the mechanism of refractive regression after both myopic and hyperopic LASIK, predisposing risk factors and its clinical course. In addition, current preventative strategies and therapies are also reviewed. © 2018 Royal Australian and New Zealand College of Ophthalmologists.
Influence diagnostics in meta-regression model.
Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua
2017-09-01
This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.
Principal component regression for crop yield estimation
Suryanarayana, T M V
2016-01-01
This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...
Regression Models for Market-Shares
DEFF Research Database (Denmark)
Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue
2005-01-01
On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....
Quasi-experimental evidence on tobacco tax regressivity.
Koch, Steven F
2018-01-01
Tobacco taxes are known to reduce tobacco consumption and to be regressive, such that tobacco control policy may have the perverse effect of further harming the poor. However, if tobacco consumption falls faster amongst the poor than the rich, tobacco control policy can actually be progressive. We take advantage of persistent and committed tobacco control activities in South Africa to examine the household tobacco expenditure burden. For the analysis, we make use of two South African Income and Expenditure Surveys (2005/06 and 2010/11) that span a series of such tax increases and have been matched across the years, yielding 7806 matched pairs of tobacco consuming households and 4909 matched pairs of cigarette consuming households. By matching households across the surveys, we are able to examine both the regressivity of the household tobacco burden, and any change in that regressivity, and since tobacco taxes have been a consistent component of tobacco prices, our results also relate to the regressivity of tobacco taxes. Like previous research into cigarette and tobacco expenditures, we find that the tobacco burden is regressive; thus, so are tobacco taxes. However, we find that over the five-year period considered, the tobacco burden has decreased, and, most importantly, falls less heavily on the poor. Thus, the tobacco burden and the tobacco tax is less regressive in 2010/11 than in 2005/06. Thus, increased tobacco taxes can, in at least some circumstances, reduce the financial burden that tobacco places on households. Copyright © 2017 Elsevier Ltd. All rights reserved.
How important is importance for prospective memory? A review
Directory of Open Access Journals (Sweden)
Stefan eWalter
2014-06-01
Full Text Available Forgetting to carry out an intention as planned can have serious consequences in everyday life. People sometimes even forget intentions that they consider as very important. Here, we review the literature on the impact of importance on prospective memory performance. We highlight different methods used to manipulate the importance of a prospective memory task such as providing rewards, importance relative to other ongoing activities, absolute importance, and providing social motives. Moreover, we address the relationship between importance and other factors known to affect prospective memory and ongoing task performance such as type of prospective memory task (time-, event- or activity-based, cognitive loads, and cue focality. Finally, we provide a connection to motivation, we summarize the effects of task importance and we identify important venues for future research.
How important is importance for prospective memory? A review
Walter, Stefan; Meier, Beat
2014-01-01
Forgetting to carry out an intention as planned can have serious consequences in everyday life. People sometimes even forget intentions that they consider as very important. Here, we review the literature on the impact of importance on prospective memory performance. We highlight different methods used to manipulate the importance of a prospective memory task such as providing rewards, importance relative to other ongoing activities, absolute importance, and providing social motives. Moreover, we address the relationship between importance and other factors known to affect prospective memory and ongoing task performance such as type of prospective memory task (time-, event-, or activity-based), cognitive loads, and processing overlaps. Finally, we provide a connection to motivation, we summarize the effects of task importance and we identify important venues for future research. PMID:25018743
Approximating prediction uncertainty for random forest regression models
John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne
2016-01-01
Machine learning approaches such as random forest haveÂ increased for the spatial modeling and mapping of continuousÂ variables. Random forest is a non-parametric ensembleÂ approach, and unlike traditional regression approaches thereÂ is no direct quantification of prediction error. UnderstandingÂ prediction uncertainty is important when using model-basedÂ continuous maps as...
On directional multiple-output quantile regression
Czech Academy of Sciences Publication Activity Database
Paindaveine, D.; Šiman, Miroslav
2011-01-01
Roč. 102, č. 2 (2011), s. 193-212 ISSN 0047-259X R&D Projects: GA MŠk(CZ) 1M06047 Grant - others:Commision EC(BE) Fonds National de la Recherche Scientifique Institutional research plan: CEZ:AV0Z10750506 Keywords : multivariate quantile * quantile regression * multiple-output regression * halfspace depth * portfolio optimization * value-at risk Subject RIV: BA - General Mathematics Impact factor: 0.879, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/siman-0364128.pdf
Removing Malmquist bias from linear regressions
Verter, Frances
1993-01-01
Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.
Robust median estimator in logisitc regression
Czech Academy of Sciences Publication Activity Database
Hobza, T.; Pardo, L.; Vajda, Igor
2008-01-01
Roč. 138, č. 12 (2008), s. 3822-3840 ISSN 0378-3758 R&D Projects: GA MŠk 1M0572 Grant - others:Instituto Nacional de Estadistica (ES) MPO FI - IM3/136; GA MŠk(CZ) MTM 2006-06872 Institutional research plan: CEZ:AV0Z10750506 Keywords : Logistic regression * Median * Robustness * Consistency and asymptotic normality * Morgenthaler * Bianco and Yohai * Croux and Hasellbroeck Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.679, year: 2008 http://library.utia.cas.cz/separaty/2008/SI/vajda-robust%20median%20estimator%20in%20logistic%20regression.pdf
Demonstration of a Fiber Optic Regression Probe
Korman, Valentin; Polzin, Kurt A.
2010-01-01
The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for
KELEŞ, Taliha; ALTUN, Murat
2016-01-01
Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it wa...
Travis Woolley; David C. Shaw; Lisa M. Ganio; Stephen. Fitzgerald
2012-01-01
Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed bums and understanding disturbance ecology. We review literature concerning post-fire mortality prediction using logistic regression models for coniferous tree species in the western USA. We include synthesis and review of: methods to develop, evaluate...
Directory of Open Access Journals (Sweden)
Suduan Chen
2014-01-01
Full Text Available As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.
Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De
2014-01-01
As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.
Method for nonlinear exponential regression analysis
Junkin, B. G.
1972-01-01
Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.
Measurement Error in Education and Growth Regressions
Portela, Miguel; Alessie, Rob; Teulings, Coen
2010-01-01
The use of the perpetual inventory method for the construction of education data per country leads to systematic measurement error. This paper analyzes its effect on growth regressions. We suggest a methodology for correcting this error. The standard attenuation bias suggests that using these
The M Word: Multicollinearity in Multiple Regression.
Morrow-Howell, Nancy
1994-01-01
Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…
Deriving the Regression Line with Algebra
Quintanilla, John A.
2017-01-01
Exploration with spreadsheets and reliance on previous skills can lead students to determine the line of best fit. To perform linear regression on a set of data, students in Algebra 2 (or, in principle, Algebra 1) do not have to settle for using the mysterious "black box" of their graphing calculators (or other classroom technologies).…
Piecewise linear regression splines with hyperbolic covariates
International Nuclear Information System (INIS)
Cologne, John B.; Sposto, Richard
1992-09-01
Consider the problem of fitting a curve to data that exhibit a multiphase linear response with smooth transitions between phases. We propose substituting hyperbolas as covariates in piecewise linear regression splines to obtain curves that are smoothly joined. The method provides an intuitive and easy way to extend the two-phase linear hyperbolic response model of Griffiths and Miller and Watts and Bacon to accommodate more than two linear segments. The resulting regression spline with hyperbolic covariates may be fit by nonlinear regression methods to estimate the degree of curvature between adjoining linear segments. The added complexity of fitting nonlinear, as opposed to linear, regression models is not great. The extra effort is particularly worthwhile when investigators are unwilling to assume that the slope of the response changes abruptly at the join points. We can also estimate the join points (the values of the abscissas where the linear segments would intersect if extrapolated) if their number and approximate locations may be presumed known. An example using data on changing age at menarche in a cohort of Japanese women illustrates the use of the method for exploratory data analysis. (author)
Targeting: Logistic Regression, Special Cases and Extensions
Directory of Open Access Journals (Sweden)
Helmut Schaeben
2014-12-01
Full Text Available Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form. Weights-of-evidence is an ordinary logistic regression with parameters equal to the differences of the weights of evidence if all predictor variables are discrete and conditionally independent given the target variable. The hypothesis of conditional independence can be tested in terms of log-linear models. If the assumption of conditional independence is violated, the application of weights-of-evidence does not only corrupt the predicted conditional probabilities, but also their rank transform. Logistic regression models, including the interaction terms, can account for the lack of conditional independence, appropriate interaction terms compensate exactly for violations of conditional independence. Multilayer artificial neural nets may be seen as nested regression-like models, with some sigmoidal activation function. Most often, the logistic function is used as the activation function. If the net topology, i.e., its control, is sufficiently versatile to mimic interaction terms, artificial neural nets are able to account for violations of conditional independence and yield very similar results. Weights-of-evidence cannot reasonably include interaction terms; subsequent modifications of the weights, as often suggested, cannot emulate the effect of interaction terms.
Functional data analysis of generalized regression quantiles
Guo, Mengmeng
2013-11-05
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Regression testing Ajax applications : Coping with dynamism
Roest, D.; Mesbah, A.; Van Deursen, A.
2009-01-01
Note: This paper is a pre-print of: Danny Roest, Ali Mesbah and Arie van Deursen. Regression Testing AJAX Applications: Coping with Dynamism. In Proceedings of the 3rd International Conference on Software Testing, Verification and Validation (ICST’10), Paris, France. IEEE Computer Society, 2010.
Group-wise partial least square regression
Camacho, José; Saccenti, Edoardo
2018-01-01
This paper introduces the group-wise partial least squares (GPLS) regression. GPLS is a new sparse PLS technique where the sparsity structure is defined in terms of groups of correlated variables, similarly to what is done in the related group-wise principal component analysis. These groups are
Functional data analysis of generalized regression quantiles
Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl
2013-01-01
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Finite Algorithms for Robust Linear Regression
DEFF Research Database (Denmark)
Madsen, Kaj; Nielsen, Hans Bruun
1990-01-01
The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...
Function approximation with polynomial regression slines
International Nuclear Information System (INIS)
Urbanski, P.
1996-01-01
Principles of the polynomial regression splines as well as algorithms and programs for their computation are presented. The programs prepared using software package MATLAB are generally intended for approximation of the X-ray spectra and can be applied in the multivariate calibration of radiometric gauges. (author)
Assessing risk factors for periodontitis using regression
Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa
2013-10-01
Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.
Yet another look at MIDAS regression
Ph.H.B.F. Franses (Philip Hans)
2016-01-01
textabstractA MIDAS regression involves a dependent variable observed at a low frequency and independent variables observed at a higher frequency. This paper relates a true high frequency data generating process, where also the dependent variable is observed (hypothetically) at the high frequency,
Revisiting Regression in Autism: Heller's "Dementia Infantilis"
Westphal, Alexander; Schelinski, Stefanie; Volkmar, Fred; Pelphrey, Kevin
2013-01-01
Theodor Heller first described a severe regression of adaptive function in normally developing children, something he termed dementia infantilis, over one 100 years ago. Dementia infantilis is most closely related to the modern diagnosis, childhood disintegrative disorder. We translate Heller's paper, Uber Dementia Infantilis, and discuss…
Fast multi-output relevance vector regression
Ha, Youngmin
2017-01-01
This paper aims to decrease the time complexity of multi-output relevance vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output dimensions, M is the number of basis functions, and V
Regression Equations for Birth Weight Estimation using ...
African Journals Online (AJOL)
In this study, Birth Weight has been estimated from anthropometric measurements of hand and foot. Linear regression equations were formed from each of the measured variables. These simple equations can be used to estimate Birth Weight of new born babies, in order to identify those with low birth weight and referred to ...
Superquantile Regression: Theory, Algorithms, and Applications
2014-12-01
Highway, Suite 1204, Arlington, Va 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1...Navy submariners, reliability engineering, uncertainty quantification, and financial risk management . Superquantile, superquantile regression...Royset Carlos F. Borges Associate Professor of Operations Research Dissertation Supervisor Professor of Applied Mathematics Lyn R. Whitaker Javier
Measurement Error in Education and Growth Regressions
Portela, M.; Teulings, C.N.; Alessie, R.
The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations
Measurement error in education and growth regressions
Portela, Miguel; Teulings, Coen; Alessie, R.
2004-01-01
The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
transformation of independent variables in polynomial regression ...
African Journals Online (AJOL)
Ada
preferable when possible to work with a simple functional form in transformed variables rather than with a more complicated form in the original variables. In this paper, it is shown that linear transformations applied to independent variables in polynomial regression models affect the t ratio and hence the statistical ...
Multiple Linear Regression: A Realistic Reflector.
Nutt, A. T.; Batsell, R. R.
Examples of the use of Multiple Linear Regression (MLR) techniques are presented. This is done to show how MLR aids data processing and decision-making by providing the decision-maker with freedom in phrasing questions and by accurately reflecting the data on hand. A brief overview of the rationale underlying MLR is given, some basic definitions…
SPLINE LINEAR REGRESSION USED FOR EVALUATING FINANCIAL ASSETS 1
Directory of Open Access Journals (Sweden)
Liviu GEAMBAŞU
2010-12-01
Full Text Available One of the most important preoccupations of financial markets participants was and still is the problem of determining more precise the trend of financial assets prices. For solving this problem there were written many scientific papers and were developed many mathematical and statistical models in order to better determine the financial assets price trend. If until recently the simple linear models were largely used due to their facile utilization, the financial crises that affected the world economy starting with 2008 highlight the necessity of adapting the mathematical models to variation of economy. A simple to use model but adapted to economic life realities is the spline linear regression. This type of regression keeps the continuity of regression function, but split the studied data in intervals with homogenous characteristics. The characteristics of each interval are highlighted and also the evolution of market over all the intervals, resulting reduced standard errors. The first objective of the article is the theoretical presentation of the spline linear regression, also referring to scientific national and international papers related to this subject. The second objective is applying the theoretical model to data from the Bucharest Stock Exchange
Hierarchical Matching and Regression with Application to Photometric Redshift Estimation
Murtagh, Fionn
2017-06-01
This work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or `photo-z' problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.
A Powerful Test for Comparing Multiple Regression Functions.
Maity, Arnab
2012-09-01
In this article, we address the important problem of comparison of two or more population regression functions. Recently, Pardo-Fernández, Van Keilegom and González-Manteiga (2007) developed test statistics for simple nonparametric regression models: Y(ij) = θ(j)(Z(ij)) + σ(j)(Z(ij))∊(ij), based on empirical distributions of the errors in each population j = 1, … , J. In this paper, we propose a test for equality of the θ(j)(·) based on the concept of generalized likelihood ratio type statistics. We also generalize our test for other nonparametric regression setups, e.g, nonparametric logistic regression, where the loglikelihood for population j is any general smooth function [Formula: see text]. We describe a resampling procedure to obtain the critical values of the test. In addition, we present a simulation study to evaluate the performance of the proposed test and compare our results to those in Pardo-Fernández et al. (2007).
Boosting structured additive quantile regression for longitudinal childhood obesity data.
Fenske, Nora; Fahrmeir, Ludwig; Hothorn, Torsten; Rzehak, Peter; Höhle, Michael
2013-07-25
Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.
THE POWER OF LITERATURE IN EFL CLASSROOMS
Directory of Open Access Journals (Sweden)
Flora Debora Floris
2004-01-01
Full Text Available This paper proposes the importance of acknowledging literature as one of the best resources for promoting language learning in EFL (English as a Foreign Language classrooms. It reviews briefly various theoretical issues in teaching English through literature. Highlights are given to the justifications and guidelines for literature in the language classroom. Finally, the article presents examples of practical teaching and learning tasks based on one specific literary text.
A test of inflated zeros for Poisson regression models.
He, Hua; Zhang, Hui; Ye, Peng; Tang, Wan
2017-01-01
Excessive zeros are common in practice and may cause overdispersion and invalidate inference when fitting Poisson regression models. There is a large body of literature on zero-inflated Poisson models. However, methods for testing whether there are excessive zeros are less well developed. The Vuong test comparing a Poisson and a zero-inflated Poisson model is commonly applied in practice. However, the type I error of the test often deviates seriously from the nominal level, rendering serious doubts on the validity of the test in such applications. In this paper, we develop a new approach for testing inflated zeros under the Poisson model. Unlike the Vuong test for inflated zeros, our method does not require a zero-inflated Poisson model to perform the test. Simulation studies show that when compared with the Vuong test our approach not only better at controlling type I error rate, but also yield more power.
Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun
2014-12-01
Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.
Li, T.; Sun, L.; Zou, L.
2009-01-01
This study assesses the impact of government shareholding on corporate performance using a sample of 643 non-financial companies listed on the Chinese stock exchanges. In view of the controversial empirical findings in the literature and the limitations of the least squares regressions, we adopt the
A Note on Three Statistical Tests in the Logistic Regression DIF Procedure
Paek, Insu
2012-01-01
Although logistic regression became one of the well-known methods in detecting differential item functioning (DIF), its three statistical tests, the Wald, likelihood ratio (LR), and score tests, which are readily available under the maximum likelihood, do not seem to be consistently distinguished in DIF literature. This paper provides a clarifying…
Rudner, Lawrence
2016-01-01
In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows…
Classifying machinery condition using oil samples and binary logistic regression
Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.
2015-08-01
The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.
Najera-Zuloaga, Josu; Lee, Dae-Jin; Arostegui, Inmaculada
2017-01-01
Health-related quality of life has become an increasingly important indicator of health status in clinical trials and epidemiological research. Moreover, the study of the relationship of health-related quality of life with patients and disease characteristics has become one of the primary aims of many health-related quality of life studies. Health-related quality of life scores are usually assumed to be distributed as binomial random variables and often highly skewed. The use of the beta-binomial distribution in the regression context has been proposed to model such data; however, the beta-binomial regression has been performed by means of two different approaches in the literature: (i) beta-binomial distribution with a logistic link; and (ii) hierarchical generalized linear models. None of the existing literature in the analysis of health-related quality of life survey data has performed a comparison of both approaches in terms of adequacy and regression parameter interpretation context. This paper is motivated by the analysis of a real data application of health-related quality of life outcomes in patients with Chronic Obstructive Pulmonary Disease, where the use of both approaches yields to contradictory results in terms of covariate effects significance and consequently the interpretation of the most relevant factors in health-related quality of life. We present an explanation of the results in both methodologies through a simulation study and address the need to apply the proper approach in the analysis of health-related quality of life survey data for practitioners, providing an R package.
Variable Selection for Regression Models of Percentile Flows
Fouad, G.
2017-12-01
Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high
Corporate prediction models, ratios or regression analysis?
Bijnen, E.J.; Wijn, M.F.C.M.
1994-01-01
The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.
Controlling attribute effect in linear regression
Calders, Toon; Karim, Asim A.; Kamiran, Faisal; Ali, Wasif Mohammad; Zhang, Xiangliang
2013-01-01
In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.
Stochastic development regression using method of moments
DEFF Research Database (Denmark)
Kühnel, Line; Sommer, Stefan Horst
2017-01-01
This paper considers the estimation problem arising when inferring parameters in the stochastic development regression model for manifold valued non-linear data. Stochastic development regression captures the relation between manifold-valued response and Euclidean covariate variables using...... the stochastic development construction. It is thereby able to incorporate several covariate variables and random effects. The model is intrinsically defined using the connection of the manifold, and the use of stochastic development avoids linearizing the geometry. We propose to infer parameters using...... the Method of Moments procedure that matches known constraints on moments of the observations conditional on the latent variables. The performance of the model is investigated in a simulation example using data on finite dimensional landmark manifolds....
Beta-binomial regression and bimodal utilization.
Liu, Chuan-Fen; Burgess, James F; Manning, Willard G; Maciejewski, Matthew L
2013-10-01
To illustrate how the analysis of bimodal U-shaped distributed utilization can be modeled with beta-binomial regression, which is rarely used in health services research. Veterans Affairs (VA) administrative data and Medicare claims in 2001-2004 for 11,123 Medicare-eligible VA primary care users in 2000. We compared means and distributions of VA reliance (the proportion of all VA/Medicare primary care visits occurring in VA) predicted from beta-binomial, binomial, and ordinary least-squares (OLS) models. Beta-binomial model fits the bimodal distribution of VA reliance better than binomial and OLS models due to the nondependence on normality and the greater flexibility in shape parameters. Increased awareness of beta-binomial regression may help analysts apply appropriate methods to outcomes with bimodal or U-shaped distributions. © Health Research and Educational Trust.
Testing homogeneity in Weibull-regression models.
Bolfarine, Heleno; Valença, Dione M
2005-10-01
In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.
Are increases in cigarette taxation regressive?
Borren, P; Sutton, M
1992-12-01
Using the latest published data from Tobacco Advisory Council surveys, this paper re-evaluates the question of whether or not increases in cigarette taxation are regressive in the United Kingdom. The extended data set shows no evidence of increasing price-elasticity by social class as found in a major previous study. To the contrary, there appears to be no clear pattern in the price responsiveness of smoking behaviour across different social classes. Increases in cigarette taxation, while reducing smoking levels in all groups, fall most heavily on men and women in the lowest social class. Men and women in social class five can expect to pay eight and eleven times more of a tax increase respectively, than their social class one counterparts. Taken as a proportion of relative incomes, the regressive nature of increases in cigarette taxation is even more pronounced.
Controlling attribute effect in linear regression
Calders, Toon
2013-12-01
In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Confidence bands for inverse regression models
International Nuclear Information System (INIS)
Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo
2010-01-01
We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract
Regressing Atherosclerosis by Resolving Plaque Inflammation
2017-07-01
regression requires the alteration of macrophages in the plaques to a tissue repair “alternatively” activated state. This switch in activation state... tissue repair “alternatively” activated state. This switch in activation state requires the action of TH2 cytokines interleukin (IL)-4 or IL-13. To...regulation of tissue macrophage and dendritic cell population dynamics by CSF-1. J Exp Med. 2011;208(9):1901–1916. 35. Xu H, Exner BG, Chilton PM
Determination of regression laws: Linear and nonlinear
International Nuclear Information System (INIS)
Onishchenko, A.M.
1994-01-01
A detailed mathematical determination of regression laws is presented in the article. Particular emphasis is place on determining the laws of X j on X l to account for source nuclei decay and detector errors in nuclear physics instrumentation. Both linear and nonlinear relations are presented. Linearization of 19 functions is tabulated, including graph, relation, variable substitution, obtained linear function, and remarks. 6 refs., 1 tab
Directional quantile regression in Octave (and MATLAB)
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2016-01-01
Roč. 52, č. 1 (2016), s. 28-51 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : quantile regression * multivariate quantile * depth contour * Matlab Subject RIV: IN - Informatics, Computer Science Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2016/SI/bocek-0458380.pdf
Logistic regression a self-learning text
Kleinbaum, David G
1994-01-01
This textbook provides students and professionals in the health sciences with a presentation of the use of logistic regression in research. The text is self-contained, and designed to be used both in class or as a tool for self-study. It arises from the author's many years of experience teaching this material and the notes on which it is based have been extensively used throughout the world.
Regression Model to Predict Global Solar Irradiance in Malaysia
Directory of Open Access Journals (Sweden)
Hairuniza Ahmed Kutty
2015-01-01
Full Text Available A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE, mean bias error (MBE, and the coefficient of determination (R2 with other models available from literature studies. Seven models based on single parameters (PM1 to PM7 and five multiple-parameter models (PM7 to PM12 are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%, R2 ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.
Hierarchical Neural Regression Models for Customer Churn Prediction
Directory of Open Access Journals (Sweden)
Golshan Mohammadi
2013-01-01
Full Text Available As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN, self-organizing maps (SOM, alpha-cut fuzzy c-means (α-FCM, and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, and α-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, the α-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.
Classification of mislabelled microarrays using robust sparse logistic regression.
Bootkrajang, Jakramate; Kabán, Ata
2013-04-01
Previous studies reported that labelling errors are not uncommon in microarray datasets. In such cases, the training set may become misleading, and the ability of classifiers to make reliable inferences from the data is compromised. Yet, few methods are currently available in the bioinformatics literature to deal with this problem. The few existing methods focus on data cleansing alone, without reference to classification, and their performance crucially depends on some tuning parameters. In this article, we develop a new method to detect mislabelled arrays simultaneously with learning a sparse logistic regression classifier. Our method may be seen as a label-noise robust extension of the well-known and successful Bayesian logistic regression classifier. To account for possible mislabelling, we formulate a label-flipping process as part of the classifier. The regularization parameter is automatically set using Bayesian regularization, which not only saves the computation time that cross-validation would take, but also eliminates any unwanted effects of label noise when setting the regularization parameter. Extensive experiments with both synthetic data and real microarray datasets demonstrate that our approach is able to counter the bad effects of labelling errors in terms of predictive performance, it is effective at identifying marker genes and simultaneously it detects mislabelled arrays to high accuracy. The code is available from http://cs.bham.ac.uk/∼jxb008. Supplementary data are available at Bioinformatics online.
Modeling and prediction of flotation performance using support vector regression
Directory of Open Access Journals (Sweden)
Despotović Vladimir
2017-01-01
Full Text Available Continuous efforts have been made in recent year to improve the process of paper recycling, as it is of critical importance for saving the wood, water and energy resources. Flotation deinking is considered to be one of the key methods for separation of ink particles from the cellulose fibres. Attempts to model the flotation deinking process have often resulted in complex models that are difficult to implement and use. In this paper a model for prediction of flotation performance based on Support Vector Regression (SVR, is presented. Representative data samples were created in laboratory, under a variety of practical control variables for the flotation deinking process, including different reagents, pH values and flotation residence time. Predictive model was created that was trained on these data samples, and the flotation performance was assessed showing that Support Vector Regression is a promising method even when dataset used for training the model is limited.
Multitask Quantile Regression under the Transnormal Model.
Fan, Jianqing; Xue, Lingzhou; Zou, Hui
2016-01-01
We consider estimating multi-task quantile regression under the transnormal model, with focus on high-dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based ℓ 1 penalization with positive definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of alternating direction method of multipliers, nearest correlation matrix projection is introduced that inherits sampling properties of the unprojected one. Our work combines strengths of quantile regression and rank-based covariance regularization to simultaneously deal with nonlinearity and nonnormality for high-dimensional regression. Furthermore, the proposed method strikes a good balance between robustness and efficiency, achieves the "oracle"-like convergence rate, and provides the provable prediction interval under the high-dimensional setting. The finite-sample performance of the proposed method is also examined. The performance of our proposed rank-based method is demonstrated in a real application to analyze the protein mass spectroscopy data.
Complex regression Doppler optical coherence tomography
Elahi, Sahar; Gu, Shi; Thrane, Lars; Rollins, Andrew M.; Jenkins, Michael W.
2018-04-01
We introduce a new method to measure Doppler shifts more accurately and extend the dynamic range of Doppler optical coherence tomography (OCT). The two-point estimate of the conventional Doppler method is replaced with a regression that is applied to high-density B-scans in polar coordinates. We built a high-speed OCT system using a 1.68-MHz Fourier domain mode locked laser to acquire high-density B-scans (16,000 A-lines) at high enough frame rates (˜100 fps) to accurately capture the dynamics of the beating embryonic heart. Flow phantom experiments confirm that the complex regression lowers the minimum detectable velocity from 12.25 mm / s to 374 μm / s, whereas the maximum velocity of 400 mm / s is measured without phase wrapping. Complex regression Doppler OCT also demonstrates higher accuracy and precision compared with the conventional method, particularly when signal-to-noise ratio is low. The extended dynamic range allows monitoring of blood flow over several stages of development in embryos without adjusting the imaging parameters. In addition, applying complex averaging recovers hidden features in structural images.
Linear regression and the normality assumption.
Schmidt, Amand F; Finan, Chris
2017-12-16
Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Modeling oil production based on symbolic regression
International Nuclear Information System (INIS)
Yang, Guangfei; Li, Xianneng; Wang, Jianliang; Lian, Lian; Ma, Tieju
2015-01-01
Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans. -- Highlights: •A data-driven approach has been shown to be effective at modeling the oil production. •The Hubbert model could be discovered automatically from data. •The peak of world oil production is predicted to appear in 2021. •The decline rate after peak is half of the increase rate before peak. •Oil production projected to decline 4% post-peak
Geographically weighted regression model on poverty indicator
Slamet, I.; Nugroho, N. F. T. A.; Muslich
2017-12-01
In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.
Mixed-effects regression models in linguistics
Heylen, Kris; Geeraerts, Dirk
2018-01-01
When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addres...
On logistic regression analysis of dichotomized responses.
Lu, Kaifeng
2017-01-01
We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
General regression and representation model for classification.
Directory of Open Access Journals (Sweden)
Jianjun Qian
Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.
Image superresolution using support vector regression.
Ni, Karl S; Nguyen, Truong Q
2007-06-01
A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results.
RISKS MANAGEMENT: NEW LITERATURE REVIEW
Ennouri Wissem
2013-01-01
The complexity of the industrial activities and the important mass of flows crossing the supply chain promotes the emergence of risks that must be considered in the decision process. For this reason, we have developed this paper to clarify the basics of risk management through a short new suggestion of literature review for risk management. Our justification of this attempt is that this area is the most discussed in our days and it is impossible to present all definition of the risk concept, ...
Hahn, Andrew D; Rowe, Daniel B
2012-02-01
As more evidence is presented suggesting that the phase, as well as the magnitude, of functional MRI (fMRI) time series may contain important information and that there are theoretical drawbacks to modeling functional response in the magnitude alone, removing noise in the phase is becoming more important. Previous studies have shown that retrospective correction of noise from physiologic sources can remove significant phase variance and that dynamic main magnetic field correction and regression of estimated motion parameters also remove significant phase fluctuations. In this work, we investigate the performance of physiologic noise regression in a framework along with correction for dynamic main field fluctuations and motion regression. Our findings suggest that including physiologic regressors provides some benefit in terms of reduction in phase noise power, but it is small compared to the benefit of dynamic field corrections and use of estimated motion parameters as nuisance regressors. Additionally, we show that the use of all three techniques reduces phase variance substantially, removes undesirable spatial phase correlations and improves detection of the functional response in magnitude and phase. Copyright © 2011 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Jafri, Y.Z.; Kamal, L.
2007-01-01
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Whole-genome regression and prediction methods applied to plant and animal breeding
Los Campos, De G.; Hickey, J.M.; Pong-Wong, R.; Daetwyler, H.D.; Calus, M.P.L.
2013-01-01
Genomic-enabled prediction is becoming increasingly important in animal and plant breeding, and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of
Urananite leaching: literature survey
Energy Technology Data Exchange (ETDEWEB)
Grisham, G.F.; Bryant, E.A.; Williams, K.E.
1979-04-01
A literature survey was undertaken to provide background materials for a series of experiments involving the interaction of spent uranium dioxide fuel with various environments. Notes and references pertaining to the basic properties of UO/sub 2/ as produced and after reactor exposure are presented. The use of computerized literature searches is illustrated with specific topics related to leaching experiments. 57 references.
International Nuclear Information System (INIS)
Wanderer, J.A.
1991-01-01
The booklet is to help with the acquisition of original literature either after a conventional literature search or in particular after a database search. It bridges the gap between abbreviated (short) and original (long) titel. This, together with information on the holdings of technical/scientific libraries, facilitates document delivery. 1500 short titles are listed alphabetically. (orig.) [de
DEFF Research Database (Denmark)
Vilslev, Annette Thorsen
The PhD dissertation compares the literary theory and novels of modern Japanese writer Natsume Sōseki. It reads Sōseki’s Theory of Literature (2009, Bungakuron, 1907) as an inherently comparative and interdisciplinary approach to theorizing feelings in world literature. More broadly, the disserta......The PhD dissertation compares the literary theory and novels of modern Japanese writer Natsume Sōseki. It reads Sōseki’s Theory of Literature (2009, Bungakuron, 1907) as an inherently comparative and interdisciplinary approach to theorizing feelings in world literature. More broadly......, the dissertation investigates the critical negotiation of the novel as a travelling genre in Japan in the beginning of the 20th century, and, more specifically, Sōseki’s work in relation to world literature and affect theory. Sōseki’s work is highly influential in Japan and East Asia, and his novels widely...... circulated beyond Japan. Using Sōseki’s theory as an example, and by comparing it to other theories, the dissertation argues that comparative literature needs to include not only more non-Western literature but also more non-Western literary theories in the ongoing debate of world literature. Close...
Eidsvik, Charles Vernon
The cinema stemmed from aesthetic and formal quests within printed literature, and restored to literature the traditions of performance which had been submerged by traditions of print. The literary identity of the cinema has been obscured by a lack of visible similarities between the cinema and modern writing. Differences between modern writing…
Directory of Open Access Journals (Sweden)
Todor Hristov
2015-01-01
Full Text Available The traumatic question “what has Bulgarian literature given to the world” acquired particular intensity in periods of crisis such as the Balkan Wars, and after 1989 and the subsequent Bulgarian EU accession. It is generally accepted that the value that Bulgarian literature transmits to the world lies in the identity it represented. The goal of the paper is to show that Bulgarian literature was constituted as a gift responding to the gift of world literature, yet ever unable to repay the debt incurred by its initial gift, and trying to alleviate its indebtedness by means of a specific language of exchange. Hristov believes that studying the literature on the value of Bulgarian literature will demonstrate that the notions of identity, recognition, value, translation, national and world literature have been inscribed in a scriptural economy blending gift and exchange in a peculiar way. He hopes that this economy emerged as a modification of the scriptural economy in which the notion of world literature had been embedded, and that it has been globalised into a universal literary economy.
DEFF Research Database (Denmark)
Vestergaard, Jens; Linneberg, Mai Skjøtt; Nielsen, Robert Green
2001-01-01
Gives an overvie of the situation with respect to organic and conversion markets in Denmark based on exsisting literature. The following subjects are covered. National Policies. Agricultural Production. Conversion. Agricultural Marketing......Gives an overvie of the situation with respect to organic and conversion markets in Denmark based on exsisting literature. The following subjects are covered. National Policies. Agricultural Production. Conversion. Agricultural Marketing...
Urananite leaching: literature survey
International Nuclear Information System (INIS)
Grisham, G.F.; Bryant, E.A.; Williams, K.E.
1979-04-01
A literature survey was undertaken to provide background materials for a series of experiments involving the interaction of spent uranium dioxide fuel with various environments. Notes and references pertaining to the basic properties of UO 2 as produced and after reactor exposure are presented. The use of computerized literature searches is illustrated with specific topics related to leaching experiments. 57 references
Empowering Students through Literature.
Cukras, Grace-Ann Gorga
2000-01-01
A literary club formed a community of readers among underserved and nontraditional community college students. Members meet to discuss literature and host authors' visits. The environment enables students to share their perspectives and develop deeper understanding of literature and of themselves. (SK)
DEFF Research Database (Denmark)
A collection of essays published in the journal Literature and Theology based on selected papers from the 2012 international conference of the International Society of Religion, Literature and Culture: Cultures of Transition: Presence, Absence, Memory, held at the Faculty of Theology in Copenhagen...
Evolution of Modularity Literature
DEFF Research Database (Denmark)
Frandsen, Thomas
2017-01-01
Purpose The purpose of this paper is to review and analyze the modularity literature to identify the established and emerging perspectives. Design/methodology/approach A systematic literature search and review was conducted through the use of bibliometrics and network analysis. The analysis...... identified structure within the literature, which revealed how the research area evolved between 1990 and 2015. Based on this search, the paper establishes the basis for analyzing the structure of modularity literature. Findings Factors were identified within the literature, demonstrating how it has evolved...... from a primary focus on the modularity of products to a broader view of the applicability of modularity. Within the last decade, numerous research areas have emerged within the broader area of modularity. Through core-periphery analysis, eight emerging sub-research areas are identified, of which one...
Primary Identity in Literature
DEFF Research Database (Denmark)
Graham, Brian Russell
In our times, literary criticism, as well as larger political and cultural developments, is characterized by identity politics, meaning that our discourses are structured around the notion of different socially identifiable populations in society. In relation to literature, this results in our...... viewing the characters in literature in terms of these political identities. Literature is consequently discussed in relation to political causes. Literary criticism is animated by the same causes, and is viewed as having a direct intervention in society in relation to them. In this paper, I will discuss......, in relation to Frye’s works, the idea that the primary identities of characters in literature were and, to a considerable extent, continue to be those of family-member identities. As such, literature should not be appropriated to a political context too readily. Whereas viewing characters in terms of...
Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M
2007-09-01
Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.
A method for nonlinear exponential regression analysis
Junkin, B. G.
1971-01-01
A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.
Multinomial logistic regression in workers' health
Grilo, Luís M.; Grilo, Helena L.; Gonçalves, Sónia P.; Junça, Ana
2017-11-01
In European countries, namely in Portugal, it is common to hear some people mentioning that they are exposed to excessive and continuous psychosocial stressors at work. This is increasing in diverse activity sectors, such as, the Services sector. A representative sample was collected from a Portuguese Services' organization, by applying a survey (internationally validated), which variables were measured in five ordered categories in Likert-type scale. A multinomial logistic regression model is used to estimate the probability of each category of the dependent variable general health perception where, among other independent variables, burnout appear as statistically significant.
Three Contributions to Robust Regression Diagnostics
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2015-01-01
Roč. 11, č. 2 (2015), s. 69-78 ISSN 1336-9180 Grant - others:GA ČR(CZ) GA13-01930S; Nadační fond na podporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : robust regression * robust econometrics * hypothesis test ing Subject RIV: BA - General Mathematics http://www.degruyter.com/view/j/jamsi.2015.11.issue-2/jamsi-2015-0013/jamsi-2015-0013.xml?format=INT
SDE based regression for random PDEs
Bayer, Christian
2016-01-01
A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
Bayesian regression of piecewise homogeneous Poisson processes
Directory of Open Access Journals (Sweden)
Diego Sevilla
2015-12-01
Full Text Available In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data. The resulting method is valuable for detecting breaking points in the count rate of time series for Poisson processes. Received: 2 November 2015, Accepted: 27 November 2015; Edited by: R. Dickman; Reviewed by: M. Hutter, Australian National University, Canberra, Australia.; DOI: http://dx.doi.org/10.4279/PIP.070018 Cite as: D J R Sevilla, Papers in Physics 7, 070018 (2015
Selecting a Regression Saturated by Indicators
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest....
Selecting a Regression Saturated by Indicators
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest...
Mapping geogenic radon potential by regression kriging
Energy Technology Data Exchange (ETDEWEB)
Pásztor, László [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Szabó, Katalin Zsuzsanna, E-mail: sz_k_zs@yahoo.de [Department of Chemistry, Institute of Environmental Science, Szent István University, Páter Károly u. 1, Gödöllő 2100 (Hungary); Szatmári, Gábor; Laborczi, Annamária [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Horváth, Ákos [Department of Atomic Physics, Eötvös University, Pázmány Péter sétány 1/A, 1117 Budapest (Hungary)
2016-02-15
Radon ({sup 222}Rn) gas is produced in the radioactive decay chain of uranium ({sup 238}U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method
Fixed kernel regression for voltammogram feature extraction
International Nuclear Information System (INIS)
Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N
2009-01-01
Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals
SDE based regression for random PDEs
Bayer, Christian
2016-01-06
A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
Neutrosophic Correlation and Simple Linear Regression
Directory of Open Access Journals (Sweden)
A. A. Salama
2014-09-01
Full Text Available Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache. Recently, Salama et al., introduced the concept of correlation coefficient of neutrosophic data. In this paper, we introduce and study the concepts of correlation and correlation coefficient of neutrosophic data in probability spaces and study some of their properties. Also, we introduce and study the neutrosophic simple linear regression model. Possible applications to data processing are touched upon.
Spectral density regression for bivariate extremes
Castro Camilo, Daniela
2016-05-11
We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods. © 2016 Springer-Verlag Berlin Heidelberg
SPE dose prediction using locally weighted regression
International Nuclear Information System (INIS)
Hines, J. W.; Townsend, L. W.; Nichols, T. F.
2005-01-01
When astronauts are outside earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events (SPEs). The total dose received from a major SPE in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be mitigated with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train and provides predictions as accurate as neural network models previously used. (authors)
Mapping geogenic radon potential by regression kriging
International Nuclear Information System (INIS)
Pásztor, László; Szabó, Katalin Zsuzsanna; Szatmári, Gábor; Laborczi, Annamária; Horváth, Ákos
2016-01-01
Radon ( 222 Rn) gas is produced in the radioactive decay chain of uranium ( 238 U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method, regression
SPE dose prediction using locally weighted regression
International Nuclear Information System (INIS)
Hines, J. W.; Townsend, L. W.; Nichols, T. F.
2005-01-01
When astronauts are outside Earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events. The total dose received from a major solar particle event in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be reduced with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train, and provides predictions as accurate as the neural network models that were used previously. (authors)
AIRLINE ACTIVITY FORECASTING BY REGRESSION MODELS
Directory of Open Access Journals (Sweden)
Н. Білак
2012-04-01
Full Text Available Proposed linear and nonlinear regression models, which take into account the equation of trend and seasonality indices for the analysis and restore the volume of passenger traffic over the past period of time and its prediction for future years, as well as the algorithm of formation of these models based on statistical analysis over the years. The desired model is the first step for the synthesis of more complex models, which will enable forecasting of passenger (income level airline with the highest accuracy and time urgency.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Guns, M.; Vanacker, Veerle
2012-01-01
Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logisti...
Logistic Regression in the Identification of Hazards in Construction
Drozd, Wojciech
2017-10-01
The construction site and its elements create circumstances that are conducive to the formation of risks to safety during the execution of works. Analysis indicates the critical importance of these factors in the set of characteristics that describe the causes of accidents in the construction industry. This article attempts to analyse the characteristics related to the construction site, in order to indicate their importance in defining the circumstances of accidents at work. The study includes sites inspected in 2014 - 2016 by the employees of the District Labour Inspectorate in Krakow (Poland). The analysed set of detailed (disaggregated) data includes both quantitative and qualitative characteristics. The substantive task focused on classification modelling in the identification of hazards in construction and identifying those of the analysed characteristics that are important in an accident. In terms of methodology, resource data analysis using statistical classifiers, in the form of logistic regression, was the method used.
Testing and Modeling Fuel Regression Rate in a Miniature Hybrid Burner
Directory of Open Access Journals (Sweden)
Luciano Fanton
2012-01-01
Full Text Available Ballistic characterization of an extended group of innovative HTPB-based solid fuel formulations for hybrid rocket propulsion was performed in a lab-scale burner. An optical time-resolved technique was used to assess the quasisteady regression history of single perforation, cylindrical samples. The effects of metalized additives and radiant heat transfer on the regression rate of such formulations were assessed. Under the investigated operating conditions and based on phenomenological models from the literature, analyses of the collected experimental data show an appreciable influence of the radiant heat flux from burnt gases and soot for both unloaded and loaded fuel formulations. Pure HTPB regression rate data are satisfactorily reproduced, while the impressive initial regression rates of metalized formulations require further assessment.
Ernst, Anja F.
2017-01-01
Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking. PMID:28533971
Directory of Open Access Journals (Sweden)
Anja F. Ernst
2017-05-01
Full Text Available Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.
Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon
2015-01-01
Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.
Teaching English Through Literature
Directory of Open Access Journals (Sweden)
Murat Hişmanoğlu
2005-04-01
Full Text Available This paper aims at emphasizing the use of literature as a popular technique for teaching both basiclanguage skills (i.e. reading, writing, listening and speaking and language areas (i.e. vocabulary,grammar and pronunciation in our times. Reasons for using literary texts in foreign language classroomand main criteria for selecting suitable literary texts in foreign language classes are stressed so as tomake the reader familiar with the underlying reasons and criteria for language teachers’ using andselecting literary texts. Moreover, literature and the teaching of language skills, benefits of differentgenres of literature (i.e. poetry, short fiction, drama and novel to language teaching and some problemsencountered by language teachers within the area of teaching English through literature (i.e. lack ofpreparation in the area of literature teaching in TESL / TEFL programs, absence of clear-cut objectivesdefining the role of literature in ESL / EFL, language teachers’ not having the background and trainingin literature, lack of pedagogically-designed appropriate materials that can be used by language teachersin a classroom context are taken into account.
Moral education through literature
Directory of Open Access Journals (Sweden)
Pantić Nataša
2006-01-01
Full Text Available This paper examines a variety of perspectives on the role of literature in moral education. These proceed from general considerations to more specific issues that remain contested to the present day, such as distinction between individual and social morality. Others bring any literature under suspicion in the post-structuralist era, such as the cultural relativity of morality, distinctions between aesthetic and moral dimensions of literary works, and between moral awareness and behavior. The discussion is illustrated through considerations of the place of literature in English moral education from the Victorians to the present day. The discussion of dilemmas that policy makers and educators face today focuses on three dilemmas that often serve to question a possibility of justifying the morally educative power of literature: cultural relativism in literature and ideology (and its implications for the canon, the distinction between an aesthetic and moral power of literature, and finally, the doubts about the transferability of moral awareness acquired through literature to actual moral conduct. .
Optimized support vector regression for drilling rate of penetration estimation
Bodaghi, Asadollah; Ansari, Hamid Reza; Gholami, Mahsa
2015-12-01
In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called `optimized support vector regression' is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.
Predicting company growth using logistic regression and neural networks
Directory of Open Access Journals (Sweden)
Marijana Zekić-Sušac
2016-12-01
Full Text Available The paper aims to establish an efficient model for predicting company growth by leveraging the strengths of logistic regression and neural networks. A real dataset of Croatian companies was used which described the relevant industry sector, financial ratios, income, and assets in the input space, with a dependent binomial variable indicating whether a company had high-growth if it had annualized growth in assets by more than 20% a year over a three-year period. Due to a large number of input variables, factor analysis was performed in the pre -processing stage in order to extract the most important input components. Building an efficient model with a high classification rate and explanatory ability required application of two data mining methods: logistic regression as a parametric and neural networks as a non -parametric method. The methods were tested on the models with and without variable reduction. The classification accuracy of the models was compared using statistical tests and ROC curves. The results showed that neural networks produce a significantly higher classification accuracy in the model when incorporating all available variables. The paper further discusses the advantages and disadvantages of both approaches, i.e. logistic regression and neural networks in modelling company growth. The suggested model is potentially of benefit to investors and economic policy makers as it provides support for recognizing companies with growth potential, especially during times of economic downturn.
Estimation of adjusted rate differences using additive negative binomial regression.
Donoghoe, Mark W; Marschner, Ian C
2016-08-15
Rate differences are an important effect measure in biostatistics and provide an alternative perspective to rate ratios. When the data are event counts observed during an exposure period, adjusted rate differences may be estimated using an identity-link Poisson generalised linear model, also known as additive Poisson regression. A problem with this approach is that the assumption of equality of mean and variance rarely holds in real data, which often show overdispersion. An additive negative binomial model is the natural alternative to account for this; however, standard model-fitting methods are often unable to cope with the constrained parameter space arising from the non-negativity restrictions of the additive model. In this paper, we propose a novel solution to this problem using a variant of the expectation-conditional maximisation-either algorithm. Our method provides a reliable way to fit an additive negative binomial regression model and also permits flexible generalisations using semi-parametric regression functions. We illustrate the method using a placebo-controlled clinical trial of fenofibrate treatment in patients with type II diabetes, where the outcome is the number of laser therapy courses administered to treat diabetic retinopathy. An R package is available that implements the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Approximate median regression for complex survey data with skewed response.
Fraser, Raphael André; Lipsitz, Stuart R; Sinha, Debajyoti; Fitzmaurice, Garrett M; Pan, Yi
2016-12-01
The ready availability of public-use data from various large national complex surveys has immense potential for the assessment of population characteristics using regression models. Complex surveys can be used to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data due to complex survey design features. That is, stratification, multistage sampling, and weighting. In this article, we accommodate these design features in the analysis of highly skewed response variables arising from large complex surveys. Specifically, we propose a double-transform-both-sides (DTBS)'based estimating equations approach to estimate the median regression parameters of the highly skewed response; the DTBS approach applies the same Box-Cox type transformation twice to both the outcome and regression function. The usual sandwich variance estimate can be used in our approach, whereas a resampling approach would be needed for a pseudo-likelihood based on minimizing absolute deviations (MAD). Furthermore, the approach is relatively robust to the true underlying distribution, and has much smaller mean square error than a MAD approach. The method is motivated by an analysis of laboratory data on urinary iodine (UI) concentration from the National Health and Nutrition Examination Survey. © 2016, The International Biometric Society.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak; Ghosh, Malay; Mallick, Bani K.
2012-01-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik's ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Spontaneous regression of intracranial malignant lymphoma
International Nuclear Information System (INIS)
Kojo, Nobuto; Tokutomi, Takashi; Eguchi, Gihachirou; Takagi, Shigeyuki; Matsumoto, Tomie; Sasaguri, Yasuyuki; Shigemori, Minoru.
1988-01-01
In a 46-year-old female with a 1-month history of gait and speech disturbances, computed tomography (CT) demonstrated mass lesions of slightly high density in the left basal ganglia and left frontal lobe. The lesions were markedly enhanced by contrast medium. The patient received no specific treatment, but her clinical manifestations gradually abated and the lesions decreased in size. Five months after her initial examination, the lesions were absent on CT scans; only a small area of low density remained. Residual clinical symptoms included mild right hemiparesis and aphasia. After 14 months the patient again deteriorated, and a CT scan revealed mass lesions in the right frontal lobe and the pons. However, no enhancement was observed in the previously affected regions. A biopsy revealed malignant lymphoma. Despite treatment with steroids and radiation, the patient's clinical status progressively worsened and she died 27 months after initial presentation. Seven other cases of spontaneous regression of primary malignant lymphoma have been reported. In this case, the mechanism of the spontaneous regression was not clear, but changes in immunologic status may have been involved. (author)
Regression testing in the TOTEM DCS
International Nuclear Information System (INIS)
Rodríguez, F Lucas; Atanassov, I; Burkimsher, P; Frost, O; Taskinen, J; Tulimaki, V
2012-01-01
The Detector Control System of the TOTEM experiment at the LHC is built with the industrial product WinCC OA (PVSS). The TOTEM system is generated automatically through scripts using as input the detector Product Breakdown Structure (PBS) structure and its pinout connectivity, archiving and alarm metainformation, and some other heuristics based on the naming conventions. When those initial parameters and automation code are modified to include new features, the resulting PVSS system can also introduce side-effects. On a daily basis, a custom developed regression testing tool takes the most recent code from a Subversion (SVN) repository and builds a new control system from scratch. This system is exported in plain text format using the PVSS export tool, and compared with a system previously validated by a human. A report is sent to the developers with any differences highlighted, in readiness for validation and acceptance as a new stable version. This regression approach is not dependent on any development framework or methodology. This process has been satisfactory during several months, proving to be a very valuable tool before deploying new versions in the production systems.
Supporting Regularized Logistic Regression Privately and Efficiently
Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei
2016-01-01
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738
Structural Break Tests Robust to Regression Misspecification
Directory of Open Access Journals (Sweden)
Alaa Abi Morshed
2018-05-01
Full Text Available Structural break tests for regression models are sensitive to model misspecification. We show—analytically and through simulations—that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe size distortions when the conditional mean dynamics are misspecified. We also show that the sup Wald test for breaks in the unconditional mean and variance does not have the same size distortions, yet benefits from similar power to its conditional counterpart in correctly specified models. Hence, we propose using it as an alternative and complementary test for breaks. We apply the unconditional and conditional mean and variance tests to three US series: unemployment, industrial production growth and interest rates. Both the unconditional and the conditional mean tests detect a break in the mean of interest rates. However, for the other two series, the unconditional mean test does not detect a break, while the conditional mean tests based on dynamic regression models occasionally detect a break, with the implied break-point estimator varying across different dynamic specifications. For all series, the unconditional variance does not detect a break while most tests for the conditional variance do detect a break which also varies across specifications.
Supporting Regularized Logistic Regression Privately and Efficiently.
Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei
2016-01-01
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak
2012-07-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik\\'s ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Hyperspectral Unmixing with Robust Collaborative Sparse Regression
Directory of Open Access Journals (Sweden)
Chang Li
2016-07-01
Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.
Supporting Regularized Logistic Regression Privately and Efficiently.
Directory of Open Access Journals (Sweden)
Wenfa Li
Full Text Available As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Finding determinants of audit delay by pooled OLS regression analysis
Vuko, Tina; Čular, Marko
2014-01-01
The aim of this paper is to investigate determinants of audit delay. Audit delay is measured as the length of time (i.e. the number of calendar days) from the fiscal year-end to the audit report date. It is important to understand factors that influence audit delay since it directly affects the timeliness of financial reporting. The research is conducted on a sample of Croatian listed companies, covering the period of four years (from 2008 to 2011). We use pooled OLS regression analysis, mode...
Interpret with caution: multicollinearity in multiple regression of cognitive data.
Morrison, Catriona M
2003-08-01
Shibihara and Kondo in 2002 reported a reanalysis of the 1997 Kanji picture-naming data of Yamazaki, Ellis, Morrison, and Lambon-Ralph in which independent variables were highly correlated. Their addition of the variable visual familiarity altered the previously reported pattern of results, indicating that visual familiarity, but not age of acquisition, was important in predicting Kanji naming speed. The present paper argues that caution should be taken when drawing conclusions from multiple regression analyses in which the independent variables are so highly correlated, as such multicollinearity can lead to unreliable output.
DEFF Research Database (Denmark)
Literary authors have frequently called on elements of cartography to ground fictional space, to visualize sites, and to help readers get their bearings in the imaginative world of the text. Today, the convergence of digital mapping and globalization has spurred a cartographic turn in literature...... but represents a set of relations and tensions that raise questions about representation, fiction, and space. Is literature even mappable? In exploring the cartographic components of literature, the contributors have not only brought literary theory to bear on the map but have also enriched the vocabulary...... fictions....
Wells-Lassagne, Shannon
2017-01-01
The study of “literature on screen” is not new: indeed, this terminology has long been used for the study of adaptation, perhaps most notably in Deborah Cartmell and Imelda Whelehan’s Cambridge Companion to Literature on Screen. What is less widespread, however, is the association of literature with television’s “small screen” – because of its serial storytelling, television adaptation has long been relegated to limited-run miniseries (what I’ve called short-form adaptations), and the study o...
Interpreting parameters in the logistic regression model with random effects
DEFF Research Database (Denmark)
Larsen, Klaus; Petersen, Jørgen Holm; Budtz-Jørgensen, Esben
2000-01-01
interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects......interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects...
Yoneoka, Daisuke; Henmi, Masayuki
2017-11-30
Recently, the number of clinical prediction models sharing the same regression task has increased in the medical literature. However, evidence synthesis methodologies that use the results of these regression models have not been sufficiently studied, particularly in meta-analysis settings where only regression coefficients are available. One of the difficulties lies in the differences between the categorization schemes of continuous covariates across different studies. In general, categorization methods using cutoff values are study specific across available models, even if they focus on the same covariates of interest. Differences in the categorization of covariates could lead to serious bias in the estimated regression coefficients and thus in subsequent syntheses. To tackle this issue, we developed synthesis methods for linear regression models with different categorization schemes of covariates. A 2-step approach to aggregate the regression coefficient estimates is proposed. The first step is to estimate the joint distribution of covariates by introducing a latent sampling distribution, which uses one set of individual participant data to estimate the marginal distribution of covariates with categorization. The second step is to use a nonlinear mixed-effects model with correction terms for the bias due to categorization to estimate the overall regression coefficients. Especially in terms of precision, numerical simulations show that our approach outperforms conventional methods, which only use studies with common covariates or ignore the differences between categorization schemes. The method developed in this study is also applied to a series of WHO epidemiologic studies on white blood cell counts. Copyright © 2017 John Wiley & Sons, Ltd.
International Nuclear Information System (INIS)
Vogt, Frank
2013-01-01
utilized such as literature values of concentration ranges, concentration ratios implied e.g. by stoichiometry, sum parameters to which multiple analytes need to amount to, and/or reasonable signal reconstructions. The core idea is to mitigate the regression principle's strive for the best possible explanation of measured signals toward the best possible explanation under the condition of chemical meaningfulness. As proof-of-principle application, quantitative analyses of selected compounds in microalgae cells have been chosen. After acquiring FTIR calibration spectra from concentration series of 28 analytes, an ex situ calibration model has been built via principal component regression (PCR). Since microalgae biomass is a very complex matrix, the prediction step based on such an incomplete calibration fails. However, after incorporating several regression constraints into PCR predictions, chemically impossible results are avoided as depicted in the graphical abstract. Equally important are enhancements in concentration reproducibility. For most samples in the chosen application, the errorbars were reduced by one order of magnitude. By means of this novel chemometric method, quantitative analyses have been improved so much that cell responses to chemical shifts in their culturing environment can be studied
Appendix A : literature review.
2013-03-01
This appendix contains a review of the literature and other background information : germane to the experimental and analytical research presented in subsequent appendices. Table : 1 lists the sections and topics contained in this appendix and those ...
Football injury: a literature review *
Kos, John J.
1979-01-01
A great deal of concern is recently being expressed relative to the playing of tackle football by adolescent Canadians. The purpose of this literature review is to try to summarize the important data from the available world literature. Very few Canadian statistics are available. Most of the data comes from United States experience. Tackle football injury is examined from various perspectives: 1. Equipment 2. Mechanisms of injury 3. Types of injury, with some emphasis on epiphyseal injury 4. Prevention 5. Comparison with other sports Although no “hard and fast” conclusion is drawn, the paper tends to show that: 1. Football is dangerous 2. Football is damaging to many body systems 3. Prevention of injury is difficult under present conditions 4. Alternate games, such as soccer and rugby seem to provide the same benefits with less catastrophic injuries
BANK FAILURE PREDICTION WITH LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Taha Zaghdoudi
2013-04-01
Full Text Available In recent years the economic and financial world is shaken by a wave of financial crisis and resulted in violent bank fairly huge losses. Several authors have focused on the study of the crises in order to develop an early warning model. It is in the same path that our work takes its inspiration. Indeed, we have tried to develop a predictive model of Tunisian bank failures with the contribution of the binary logistic regression method. The specificity of our prediction model is that it takes into account microeconomic indicators of bank failures. The results obtained using our provisional model show that a bank's ability to repay its debt, the coefficient of banking operations, bank profitability per employee and leverage financial ratio has a negative impact on the probability of failure.
Robust Mediation Analysis Based on Median Regression
Yuan, Ying; MacKinnon, David P.
2014-01-01
Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925
ANYOLS, Least Square Fit by Stepwise Regression
International Nuclear Information System (INIS)
Atwoods, C.L.; Mathews, S.
1986-01-01
Description of program or function: ANYOLS is a stepwise program which fits data using ordinary or weighted least squares. Variables are selected for the model in a stepwise way based on a user- specified input criterion or a user-written subroutine. The order in which variables are entered can be influenced by user-defined forcing priorities. Instead of stepwise selection, ANYOLS can try all possible combinations of any desired subset of the variables. Automatic output for the final model in a stepwise search includes plots of the residuals, 'studentized' residuals, and leverages; if the model is not too large, the output also includes partial regression and partial leverage plots. A data set may be re-used so that several selection criteria can be tried. Flexibility is increased by allowing the substitution of user-written subroutines for several default subroutines
Nonparametric additive regression for repeatedly measured data
Carroll, R. J.
2009-05-20
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Conjoined legs: Sirenomelia or caudal regression syndrome?
Directory of Open Access Journals (Sweden)
Sakti Prasad Das
2013-01-01
Full Text Available Presence of single umbilical persistent vitelline artery distinguishes sirenomelia from caudal regression syndrome. We report a case of a12-year-old boy who had bilateral umbilical arteries presented with fusion of both legs in the lower one third of leg. Both feet were rudimentary. The right foot had a valgus rocker-bottom deformity. All toes were present but rudimentary. The left foot showed absence of all toes. Physical examination showed left tibia vara. The chest evaluation in sitting revealed pigeon chest and elevated right shoulder. Posterior examination of the trunk showed thoracic scoliosis with convexity to right. The patient was operated and at 1 year followup the boy had two separate legs with a good aesthetic and functional results.
Conjoined legs: Sirenomelia or caudal regression syndrome?
Das, Sakti Prasad; Ojha, Niranjan; Ganesh, G Shankar; Mohanty, Ram Narayan
2013-07-01
Presence of single umbilical persistent vitelline artery distinguishes sirenomelia from caudal regression syndrome. We report a case of a12-year-old boy who had bilateral umbilical arteries presented with fusion of both legs in the lower one third of leg. Both feet were rudimentary. The right foot had a valgus rocker-bottom deformity. All toes were present but rudimentary. The left foot showed absence of all toes. Physical examination showed left tibia vara. The chest evaluation in sitting revealed pigeon chest and elevated right shoulder. Posterior examination of the trunk showed thoracic scoliosis with convexity to right. The patient was operated and at 1 year followup the boy had two separate legs with a good aesthetic and functional results.