Academic literature on the topic 'Relevant predictor variables'
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Journal articles on the topic "Relevant predictor variables"
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Ruzicka, Mary F. "Predictor Variables of Clergy Pedophiles." Psychological Reports 80, no. 2 (April 1997): 589–90. http://dx.doi.org/10.2466/pr0.1997.80.2.589.
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File data on familial traits, past sexual experience as a victim, and other traits identified in the literature as leading toward pedophilia, were summarized for 10 convicted clergy pedophiles to construct a set of variables possibly useful for screening. Further research is underway to identify trauma in early life and those personality-related variables current studies indicate as relevant.
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Wilson, Claire A., Sarah E. Babcock, and Donald H. Saklofske. "Sinking or Swimming in an Academic Pool: A Study of Resiliency and Student Success in First-Year Undergraduates." Canadian Journal of Higher Education 49, no. 1 (April 21, 2019): 60–84. http://dx.doi.org/10.47678/cjhe.v49i1.188220.
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The transition from high school to post-secondary education presents challenges for students. Many variables have been identified as significant predictors of student achievement. Resiliency, defined as the ability to overcome challenges and adversity, may be particularly relevant during the adjustment to post-secondary education. This study assesses whether resiliency incrementally predicts student success after controlling for additional predictors. Participants were 277 undergraduate students who completed self-reports of academic skills, resiliency, personality variables, emotional intelligence (EI), and perfectionism. Students’ year-end GPA was collected from the university registrar. Hierarchical regression analysis revealed that resiliency, measured by sense of mastery, negatively predicted GPA after controlling for other predictors. The sense of mastery facet of self-efficacy positively predicted GPA; however, the adaptability facet was a significant negative predictor of GPA. Findings suggest that self-efficacy is a salient predictor of academic success, and that strong academic skills may serve as a protective factor for poor adaptability.
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Irwin, Julie R., and Gary H. McClelland. "Negative Consequences of Dichotomizing Continuous Predictor Variables." Journal of Marketing Research 40, no. 3 (August 2003): 366–71. http://dx.doi.org/10.1509/jmkr.40.3.366.19237.
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Marketing researchers frequently split (dichotomize) continuous predictor variables into two groups, as with a median split, before performing data analysis. The practice is prevalent, but its effects are not well understood. In this article, the authors present historical results on the effects of dichotomization of normal predictor variables rederived in a regression context that may be more relevant to marketing researchers. The authors then present new results on the effect of dichotomizing continuous predictor variables with various nonnormal distributions and examine the effects of dichotomization on model specification and fit in multiple regression. The authors conclude that dichotomization has only negative consequences and should be avoided.
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Lopez, Alberto D., Kanisha Patel, Vincent P. Diego, Marcio A. Almeida, John Blangero, Jerry S. Powell, and Tom E. Howard. "On the Role of Hemostasis Variables in Cardiometabolic Outcomes." Blood 138, Supplement 1 (November 5, 2021): 4266. http://dx.doi.org/10.1182/blood-2021-154441.
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Abstract Background: Hemostasis variables represent well-known pathogenic determinants of venous-thromboembolism and are hypothesized to influence risk for cardiometabolic outcomes (CMOs) by effecting susceptibility to vascular inflammation and/or endothelial dysfunction. Methods: We investigated in Mexican American (MA)-participants of the San Antonio Family Study the effects of 20 measured hemostasis variables on CMOs including type-2 diabetes (T2D), impaired-fasting glucose (IFG), insulin resistance (IR), hypertension, obesity based on sex-specific waist circumference (OBWC), low high-density lipoprotein (Low-HDL)-cholesterol, hypertriglyceridemia, and a heart distress (HD) variable comprised of history of heart attack and/or heart surgery. The hemostasis variables included PT, aPTT, TFPI, aPC-ratio, fibrinogen, von Willebrand Factor (VWF), ADAMST-13, D-dimer, fPS, tPS, plasminogen, and factor (F)II, FV, FVII, FVIII, FX, FXI, and FXII of the coagulation system. We used a backward stepwise-regression selection approach to identify the preliminary sets of hemostasis predictors. Results: Our final regression models consisted of the final set of hemostasis variables while accounting for age and sex as covariates. For the T2D, IFG, and HD outcomes, VWF was the only significant hemostasis predictor (p<0.05). In each case, VWF was positively associated with the outcome. The hemostasis variable fibrinogen was a significant positive predictor for the IR, OBWC, and Low-HDL-cholesterol outcomes (p<0.05). For OBWC, FV was an additional significant positive predictor (p<0.05). Finally, no hemostasis variables significantly predicted hypertension or hypertriglyceridemia. Conclusions: Our results demonstrate that, among the MA-population, all measured CMOs-except hypertension and hypertriglyceridemia-are significantly influenced by individual-variability in several hemostasis parameters with most important predictors being VWF and fibrinogen. Keywords: Hemostasis; Venous-thromboembolism; Cardiovascular diseases/disorders; Metabolic syndrome; Inflammation; Endothelial dysfunction; Mexican American; Coagulation factors; von Willebrand Factor; and Fibrinogen Disclosures No relevant conflicts of interest to declare.
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Antoniou, Maria-Christina, Leah Gilbert, Justine Gross, Jean-Benoît Rossel, Céline Julie Fischer Fumeaux, Yvan Vial, and Jardena Jacqueline Puder. "Main Fetal Predictors of Adverse Neonatal Outcomes in Pregnancies with Gestational Diabetes Mellitus." Journal of Clinical Medicine 9, no. 8 (July 28, 2020): 2409. http://dx.doi.org/10.3390/jcm9082409.
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The objectives of this study were to (a) assess the utility of fetal anthropometric variables to predict the most relevant adverse neonatal outcomes in a treated population with gestational diabetes mellitus (GDM) beyond the known impact of maternal anthropometric and metabolic parameters and (b) to identify the most important fetal predictors. A total of 189 patients with GDM were included. The fetal predictors included sonographically assessed fetal weight centile (FWC), FWC > 90% and <10%, and fetal abdominal circumference centile (FACC), FACC > 90% and < 10%, at 29 0/7 to 35 6/7 weeks. Neonatal outcomes comprising neonatal weight centile (NWC), large and small for gestational age (LGA, SGA), hypoglycemia, prematurity, hospitalization for neonatal complication, and (emergency) cesarean section were evaluated. Regression analyses were conducted. Fetal variables predicted anthropometric neonatal outcomes, prematurity, cesarean section and emergency cesarean section. These associations were independent of maternal anthropometric and metabolic predictors, with the exception of cesarean section. FWC was the most significant predictor for NWC, LGA and SGA, while FACC was the most significant predictor for prematurity and FACC > 90% for emergency cesarean section. In women with GDM, third-trimester fetal anthropometric parameters have an important role in predicting adverse neonatal outcomes beyond the impact of maternal predictors.
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Messner, Jakob W., Georg J. Mayr, and Achim Zeileis. "Nonhomogeneous Boosting for Predictor Selection in Ensemble Postprocessing." Monthly Weather Review 145, no. 1 (December 16, 2016): 137–47. http://dx.doi.org/10.1175/mwr-d-16-0088.1.
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Abstract Nonhomogeneous regression is often used to statistically postprocess ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input, but other potentially useful information sources are ignored. Although it is straightforward to add further input variables, overfitting can easily deteriorate the forecast performance for increasing numbers of input variables. This paper proposes a boosting algorithm to estimate the regression coefficients, while automatically selecting the most relevant input variables by restricting the coefficients of less important variables to zero. A case study with ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) shows that this approach effectively selects important input variables to clearly improve minimum and maximum temperature predictions at five central European stations.
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Xie, Yanxi, Yuewen Li, Victor Shi, and Quan Lu. "An Orthogonal Matching Pursuit Variable Screening Algorithm for High-Dimensional Linear Regression Models." Scientific Programming 2022 (August 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/6446903.
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Variable selection plays an important role in data mining. It is crucial to filter useful variables and extract useful information in a high-dimensional setup when the number of predictor variables d tends to be much larger than the sample size n . Statistical inferences can be more precise after irrelevant variables are moved out by the screening method. This article proposes an orthogonal matching pursuit algorithm for variable screening under the high-dimensional setup. The proposed orthogonal matching pursuit method demonstrates good performance in variable screening. In particular, if the dimension of the true model is finite, OMP might discover all relevant predictors within a finite number of steps. Throughout theoretical analysis and simulations, it is confirmed that the orthogonal matching pursuit algorithm can identify relevant predictors to ensure screening consistency in variable selection. Given the sure screening property, the BIC criterion can be used to practically select the best candidate from the models generated by the OMP algorithm. Compared with the traditional orthogonal matching pursuit method, the resulting model can improve prediction accuracy and reduce computational cost by screening out the relevant variables.
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Lookingbill, Todd R., and Dean L. Urban. "Gradient analysis, the next generation: towards more plant-relevant explanatory variables." Canadian Journal of Forest Research 35, no. 7 (July 1, 2005): 1744–53. http://dx.doi.org/10.1139/x05-109.
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The long history of gradient analysis is anchored in the observation that species turnover can be described along elevation gradients. This model is unsatisfying in that elevation is not directly relevant to plants and the ubiquitous "elevation gradient" is composed of multiple intertwined environmental factors. We offer an approach to landscape-scale vegetation analysis that disentangles the elevation gradient into its constituent parts through focused field sampling and statistical analysis. We illustrate the approach for an old-growth watershed in the Oregon Western Cascades. Our initial model of this system supports the common observation that forest community types are highly associated with specific elevation bands. By replacing elevation and other crude environmental proxy variables with estimates of more direct and resource gradients (radiation, temperature, and soil moisture), we create a vegetative model with stronger explanatory power than the proxy model in both cross-validation analysis and validation using an independent data set. The resulting model is also more biologically interpretable, which provides more meaningful insight into potential forest response to environmental change (e.g., global climate change scenarios). Acquiring a better mechanistic understanding of the relationship between plant communities and environmental predictor variables presents the next great challenge to community ecologists conducting gradient studies at landscape scales.
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González, Mari Feli, David Facal, Onésimo Juncos-Rabadán, and Javier Yanguas. "Socioeconomic, emotional, and physical execution variables as predictors of cognitive performance in a Spanish sample of middle-aged and older community-dwelling participants." International Psychogeriatrics 29, no. 10 (June 29, 2017): 1669–80. http://dx.doi.org/10.1017/s1041610217001144.
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ABSTRACTBackground:Cognitive performance is not easily predicted, since different variables play an important role in the manifestation of age-related declines. The objective of this study is to analyze the predictors of cognitive performance in a Spanish sample over 50 years from a multidimensional perspective, including socioeconomic, affective, and physical variables. Some of them are well-known predictors of cognition and others are emergent variables in the study of cognition.Methods:The total sample, drawn from the “Longitudinal Study Aging in Spain (ELES)” project, consisted of 832 individuals without signs of cognitive impairment. Cognitive function was measured with tests evaluating episodic and working memory, visuomotor speed, fluency, and naming. Thirteen independent variables were selected as predictors belonging to socioeconomic, emotional, and physical execution areas. Multiple linear regressions, following the enter method, were calculated for each age group in order to study the influence of these variables in cognitive performance.Results:Education is the variable which best predicts cognitive performance in the 50–59, 60–69, and 70–79 years old groups. In the 80+ group, the best predictor is objective economic status and education does not enter in the model.Conclusions:Age-related decline can be modified by the influence of educational and socioeconomic variables. In this context, it is relevant to take into account how easy is to modify certain variables, compared to others which depend on each person's life course.
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Parasuraman, N. R., P. Janaki Ramudu, and Nusrathuunisa . "Does Lintner model of dividend payout hold good? An Empirical evidence from BSE SENSEX firms." SDMIMD Journal of Management 3, no. 2 (September 1, 2012): 63. http://dx.doi.org/10.18311/sdmimd/2012/2743.
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This study primarily investigates into as to what influenced the dividends payment of BSE constituent companies for the years 2002 through as latest as 2011. The primary model used is that of Lintner (1956) with addition of relevant factors. The study tests three models including Lintner's basic model. While dividends paid is criterion variable in all the models, basic earnings and lagged dividends are predictor variables in the first model (Lintner model, 1956), cash earnings and lagged dividends in the second model and growth opportunities (depreciation and capital expenditure) in the third model are the predictor variables. The study tests the hypotheses if the dividends paid (criterion variable) depended on basic earnings, lagged dividends, cash earnings and capital expenditure. The multiple regression analysis has been performed using SPSS 15.0 version through ENTER method for every year and for all the years on an aggregate basis across the sample companies. Significance 'F' revealed that in all the three models dividends paid depended significantly (at 5% significance level) on all predictors variables. The value of multiple 'R' indicated that the models were very strong. Coefficient of determination (R<sup>2</sup>) also revealed that the explained portion of the relationship between criterion and predictor variables has been very high and significant enough to accept the model fit. However, standardized beta co-efficients (â) and 't' statistic revealed that basic earnings, cash earnings and lagged dividends exercised highest impact on dividends paid in most of the years during the study period. On the other hand, other predictor variables, depreciation and capital expenditure, did not have any significant impact on the dividends paid. The Durbin Watson coefficient indicated that multi co-linearity among predictor variables was strong enough to accept the validity of the model almost during the entire period of the study. Thus, the results and findings of the study support the prevalence and relevance of Lintner model of dividend policy. This means that the finance manager can't afford to ignore the variables like earnings capacity and lagged dividends while framing a dividend policy.
Dissertations / Theses on the topic "Relevant predictor variables"
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Yi, Jessica Moonhee. "Identification of relevant predictors of loan default using the Elastic Net model." Thesis, 2017. http://hdl.handle.net/2440/114273.
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The timely prediction of loan default plays an important role in lending decisions and monitoring loans. However, there has been little development of models for the selection of relevant variables for the prediction of loan default. This study identifies financial and economic indicators for the forward-looking prediction of loan default by the application of a penalised regression approach, namely the Elastic Net model.
The study employs a sample of US firms with 162 loan default events in total between 1998 and 2013. The sample is sub-divided to form a Test sample and two holdout samples: one drawn from the same period as the Test sample; and one drawn from a subsequent period. The sample of non-defaulting firms is constructed using prior probabilities based on the bond default rate for each year.
The 278 potential variables, including the ten economic indicators and 268 financial ratios or summary indicators, are regularised with the application of the Elastic Net model. This process results in the extraction of the ten predictor variables, thus identified as relevant to distinguishing between defaulting and non-defaulting firms. Only one economic indicator, the interest rate, is identified as relevant to the prediction of loan default.
The prediction-usefulness of identified predictor variables are tested using the two most widely used conventional prediction models, multiple discriminant analysis (MDA) and logistic regression (Logit). The resulting MDA and Logit models are compared with Altman’s Z-score model and Ohlson’s O-score model, respectively. Both the Elastic Net prediction models provide more logical explanations of the distinctive characteristics of loan defaulting firms than the Altman’s Z-score and Ohlson’s O-score models. The Elastic Net prediction models outperform the Altman’s Z-score and Ohlson’s O-score models in the accuracy of the Type I, the Type II and the overall classification. When applied to a holdout samples within and outside the same periods, the prediction accuracy of the Elastic Net models is maintained for both defaulting and non-defaulting firms.
This thesis contributes to the loan default literature by introducing the Elastic Net model for variable selection which enhances the predictive ability of the loan default prediction model. The findings of this thesis are potentially useful to financial institutions. Identification of financial and economic predictor variables of loan default can also facilitate assessment of the credit risk of loan applicants. The findings of this thesis may also facilitate better loan default prediction for purposes of monitoring loans. Lastly, the identification of relevant predictor variables may be useful for the classification of loans in the application of the expected loss model in the preparation of financial statements.Thesis (Ph.D.) -- University of Adelaide, Business School, 2018
Books on the topic "Relevant predictor variables"
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Thompson, Norris B., and SreyRam Kuy. Multivariable Predictors of Postoperative Surgical Site Infection after General and Vascular Surgery. Edited by SreyRam Kuy. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199384075.003.0013.
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This landmark study proposed a model for predicting surgical site infections (SSI). Using logistic regression analysis, variables independently associated with increased risk of SSI were identified, which included smoking, alcohol use, comorbidities, disseminated cancer, weight loss greater than 10%, emergency surgery, and length of operative time. This chapter describes the basics of the study, including funding, year study began, year study was published, study location, who was studied, who was excluded, how many patients, study design, study intervention, follow-up, endpoints, results, and criticism and limitations. The chapter briefly reviews other relevant studies and information, gives a summary and discusses implications, and concludes with a relevant clinical case.
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Gupta, Nikhil, and Vinod H. Srihari. North American Prodrome Longitudinal Study. Edited by Ish P. Bhalla, Rajesh R. Tampi, Vinod H. Srihari, and Michael E. Hochman. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190625085.003.0045.
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This chapter provides a summary of a landmark study on schizophrenia. The question studied was “In patients identified clinically to be at high risk for psychosis, which variables (or their combinations) best predict conversion to schizophrenia or another psychotic disorder?” Starting with that question, it describes the basics of the study, including funding, study location, who was studied, how many patients, study design, study intervention, follow-up, endpoints, results, and criticism and limitations. This study demonstrates that presence of some characteristics can better prognosticate conversion of a prodromal state to a psychotic disorder. Finally, the chapter briefly reviews other relevant studies and information, discusses implications, and concludes with a relevant clinical case.
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Sidhu, Kulraj S., Mfonobong Essiet, and Maxime Cannesson. Cardiac and vascular physiology in anaesthetic practice. Edited by Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0001.
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This chapter discusses key components of cardiovascular physiology applicable to clinical practice in the field of anaesthesiology. From theory development to ground-breaking innovations, the history of cardiac and vascular anatomy, as well as physiology, is presented. Utilizing knowledge of structure and function, parameters created have allowed adequate patient clinical assessment and guided interventions. A review of concepts reveals the impact of multiple physiological variables on a patient’s haemodynamic state and the need for more accurate and efficient measurements. In particular, it is noted that a more reliable index of ventricular contractility is the end-systolic elastance rather than the ejection fraction. Constant direct preload assessment has not yet been achieved but continues to be determined through surrogate variables, and continuous cardiac output monitoring for oxygen delivery, although advancing, has limitations. Considering the effect of compound factors perioperatively, especially heart failure, modifies the goals and interventions of anaesthetists to achieve improved outcomes. Therefore, medical management prior to surgery and complete assessment through history, physical examination, and diagnostic tests are a priority. This chapter also details the expectations following volume expansion to augment haemodynamics during surgery, the concept of functional haemodynamic monitoring, and limitations to the parameters applied in assessing fluid responsiveness. Challenging the accuracy of conventional indices to predict volume status led to the use of goal-directed therapy, reducing morbidity and minimizing length of hospital stay. The mainstay of this chapter is to reinforce the relevance of advances in haemodynamic monitoring and homeostasis optimization by anaesthetists during surgery, using fundamental concepts of cardiovascular physiology.
Book chapters on the topic "Relevant predictor variables"
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Yadav, Vibha, and Satyendra Nath. "Identification of Relevant Stochastic Input Variables for Prediction of Daily PM10 Using Artificial Neural Networks." In Advances in Intelligent Systems and Computing, 23–31. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0589-4_3.
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Karami, Elmehdi, Mohamed Rafi, and Abderraouf Ridah. "Identification of Relevant Input Variables for Prediction of Output PV Power Using Artificial Neural Network Models." In Innovations in Smart Cities Applications Edition 3, 766–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37629-1_55.
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Lesiński, Wojciech, Aneta Polewko-Klim, and Witold R. Rudnicki. "Identification of Clinical Variables Relevant for Survival Prediction in Patients with Metastatic Castration-Resistant Prostate Cancer." In Trends and Innovations in Information Systems and Technologies, 607–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45691-7_57.
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Tomaselli, Venera, and Giulio Giacomo Cantone. "Multipoint vs slider: a protocol for experiments." In Proceedings e report, 91–96. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.19.
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Since the broad diffusion of Computer-Assisted survey tools (i.e. web surveys), a lively debate about innovative scales of measure arose among social scientists and practitioners. Implications are relevant for applied Statistics and evaluation research since while traditional scales collect ordinal observations, data from sliders can be interpreted as continuous. Literature, however, report excessive times of completion of the task from sliders in web surveys. This experimental protocol is aimed at testing hypotheses on the accuracy in prediction and dispersion of estimates from anonymous participants who are recruited online and randomly assigned into tasks in recognition of shades of colour. The treatment variable is two scales: a traditional multipoint 0-10 multipoint vs a slider 0-100. Shades have a unique parametrisation (true value) and participants have to guess the true value through the scale. These tasks are designed to recreate situations of uncertainty among participants while minimizing the subjective component of a perceptual assessment and maximizing information about scale-driven differences and biases. We propose to test statistical differences in the treatment variable: (i) mean absolute error from the true value (ii), time of completion of the task. To correct biases due to the variance in the number of completed tasks among participants, data about participants can be collected through both pre-tasks acceptance of web cookies and post-tasks explicit questions.
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Diestmann, Thomas, Nils Broedling, Benedict Götz, and Tobias Melz. "Surrogate Model-Based Uncertainty Quantification for a Helical Gear Pair." In Lecture Notes in Mechanical Engineering, 191–207. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_16.
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AbstractCompetitive industrial transmission systems must perform most efficiently with reference to complex requirements and conflicting key performance indicators. This design challenge translates into a high-dimensional multi-objective optimization problem that requires complex algorithms and evaluation of computationally expensive simulations to predict physical system behavior and design robustness. Crucial for the design decision-making process is the characterization, ranking, and quantification of relevant sources of uncertainties. However, due to the strict time limits of product development loops, the overall computational burden of uncertainty quantification (UQ) may even drive state-of-the-art parallel computing resources to their limits. Efficient machine learning (ML) tools and techniques emphasizing high-fidelity simulation data-driven training will play a fundamental role in enabling UQ in the early-stage development phase.This investigation surveys UQ methods with a focus on noise, vibration, and harshness (NVH) characteristics of transmission systems. Quasi-static 3D contact dynamic simulations are performed to evaluate the static transmission error (TE) of meshing gear pairs under different loading and boundary conditions. TE indicates NVH excitation and is typically used as an objective function in the early-stage design process. The limited system size allows large-scale design of experiments (DoE) and enables numerical studies of various UQ sampling and modeling techniques where the design parameters are treated as random variables associated with tolerances from manufacturing and assembly processes. The model accuracy of generalized polynomial chaos expansion (gPC) and Gaussian process regression (GPR) is evaluated and compared. The results of the methods are discussed to conclude efficient and scalable solution procedures for robust design optimization.
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Shi, Da, Shaohua Tan, and Shuzhi Sam Ge. "Automatically Identifying Predictor Variables for Stock Return Prediction." In Artificial Higher Order Neural Networks for Economics and Business, 60–78. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-897-0.ch003.
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Real-world financial systems are often nonlinear, do not follow any regular probability distribution, and comprise a large amount of financial variables. Not surprisingly, it is hard to know which variables are relevant to the prediction of the stock return based on data collected from such a system. In this chapter, we address this problem by developing a technique consisting of a top-down part using an artificial Higher Order Neural Network (HONN) model and a bottom-up part based on a Bayesian Network (BN) model to automatically identify predictor variables for the stock return prediction from a large financial variable set. Our study provides an operational guidance for using HONN and BN in selecting predictor variables from a large amount of financial variables to support the prediction of the stock return, including the prediction of future stock return value and future stock return movement trends.
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Fendel, Victoria Beatrix Maria. "Adverbial Phrases." In Coptic Interference in the Syntax of Greek Letters from Egypt, 295–322. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869173.003.0006.
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Abstract Chapter 6 examines one area of nominal syntax; that is adverbial phrases. Greek and Coptic clearly diverge in this area with Greek drawing on both prepositional phrases and plain cases, whereas Coptic can only utilize the former option. The chapter is divided into three parts: Section 6.1 applies the concept of constructions to adverbial phrases. Section 6.2 details deviations by type of error. Section 6.3 evaluates the distribution of the observed errors against the predictor variables set out in Chapter 2. Section 6.4 briefly reviews some avoidance patterns relevant to the syntax of adverbial phrases.
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Fendel, Victoria Beatrix Maria. "Discourse Markers." In Coptic Interference in the Syntax of Greek Letters from Egypt, 323–64. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869173.003.0007.
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Abstract Chapter 7 is concerned with the organization of discourse. Classical and post-classical subordinators, coordinators, and particles are considered. Discourse-organisational trends in post-classical Greek, Coptic a4nd colloquial discourse more generally coincide, such that positive transfer and/or reinforcement is more frequent than in other areas of the morphosyntax. The chapter is divided into four parts. Section 7.1 describes and contextualizes the differences between Greek and Coptic discourse organization. Section 7.2 details deviations by type of error. Section 7.3 evaluates the distribution of the observed errors against the predictor variables set out in Chapter 2. Section 7.4 presents avoidance patterns relevant to the syntax of clause-linkage.
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Fendel, Victoria Beatrix Maria. "Semi-formulaic Phrases." In Coptic Interference in the Syntax of Greek Letters from Egypt, 413—C9.P180. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869173.003.0009.
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Abstract Chapter 9 explores the semi-formulaic expressions in our texts. These semi-formulaic expressions appear in the letter body and reflect a writer’s considerations as to the structure and tone of the letter. The chapter is divided into five parts: Section 9.1 sets apart semi-formulaic expressions from formulaic expressions and introduces the concepts of signposting and hedging. Section 9.2 describes the standard patterns and established variants and variations of each relevant semi-formulaic expression. Section 9.3 contains the analysis of errors, both phraseological and grammatical. Section 9.4 evaluates the distribution of the observed errors against the predictor variables set out in Chapter 2.
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Fendel, Victoria Beatrix Maria. "Formulaic Language." In Coptic Interference in the Syntax of Greek Letters from Egypt, 365—C8.P299. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869173.003.0008.
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Abstract Chapter 8 explores the 399 formulaic sections of our texts. The epistolary frame is a fixed set of formulae, the presence of which marks a text as a ‘letter’. The chapter is divided into five parts: Section 8.1 introduces the concept of a formula and highlights the differences between the Greek and Coptic epistolary frames. Section 8.2 describes the standard patterns and established variants and variations of each relevant formula. Section 8.3 contains the analysis of errors, both phraseological and grammatical. Section 8.4 evaluates the distribution of the observed errors against the predictor variables set out in Chapter 2. Section 8.5 presents errors regarding the syntax of personal names in formulaic contexts.
Conference papers on the topic "Relevant predictor variables"
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Carvalho, Marta de, Maria Cecília Trindade, Wladimir Freitas, and Andrei Sposito. "CAN EPWORTH SLEEPINESS SCALE BE A PREDICTOR OF COGNITIVE DEFICT IN A COGNITIVE SCREENING TEST IN A COHORT OF ELDERLY FREE OF CLINICALLY MANIFEST VASCULAR BRAIN DISEASE." In XIII Meeting of Researchers on Alzheimer's Disease and Related Disorders. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1980-5764.rpda069.
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Background: The Obstructive Sleep Apnea Syndrome (OSAS) is highly prevalent among the elderly and relevant due to its cognitive impact. Objective: To evaluate an association between cognitive impairment (CI) and the presence of OSAS as assessed by the Mini Mental (MM) scale and the Ephorth Sleepiness Scale (ESS) in a population of octogens free from overt cerebral vascular disease (CVD). Methods: 137 individuals were selected. The study was approved by the ethics committee. Categorical variables were evaluated as percentages, continuous variables with normal distribution as mean ± SD and non-parametric variables as median. The subjects were not categorized into the presence or absence of CI according to the score on the MM scale according to education. In a multivariate binary logistic regression model with dependent variable CI, independent variables were incorporated according to the clinic and whether they were associated with CI in the bivariate models. All independent variables were defined in the model. Results: There was an association between high probability of OSAS by ESS and CI by MM. X2(1) = 5.34 p = 0.021. Conclusions: There was an association between high scores on the ESS and the presence of CI at the MM, even compatible for age, BMI, gender, coronary calcification, blood pressure.
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Cote, Edgar I., James Ferguson, and Nauman Tehsin. "Statistical Predictive Modelling: A Methodology to Prioritize Site Selection for Near-Neutral pH Stress Corrosion Cracking." In 2010 8th International Pipeline Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ipc2010-31646.
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Pipelines are subjected to both residual and applied tensile stresses, and can form near-neutral pH SCC (transgranular stress corrosion cracking) if the pipeline is exposed to a conducive environment and is made from a material that is susceptible to SCC. This transgranular SCC is an ongoing integrity concern for pipeline operators. As part of an SCC Integrity Management Program (IMP), it is necessary to perform integrity assessments and prioritize segments of the pipelines to manage the SCC threat. Ultrasonic crack detection in-line inspection tools have proven capable of locating SCC, but reliability of these tools is not absolute and the reduced probability of detection of subcritical flaws limits options for proactive management. Hydrostatic retesting is a very effective program for removing near-critical axial defects, such as SCC, but does not provide useful information as to the location of SCC along the pipeline. NACE Standard RP0204-2004 (SCC Direct Assessment Methodology or SCCDA) outlines factors to consider and methodologies to employ to predict where the SCC is likely to occur, but the standard acknowledges that there are no well-established methods for predicting the presence of SCC with a high degree of certainty. The trend in probabilistic modelling has been to focus on establishing deterministic relationships between environmental factors, tensile stress and SCC formation, and growth; these models have achieved varying degrees of success. The Statistical Predictive Model (SPM) was previously developed to predict the likelihood of occurrence of near-neutral pH Stress Corrosion Cracking (SCC) for the NPS 10 Alberta Products Pipeline (APPL). SPM Phase 5 uses selected predictor variables representing tensile stress, environmental, pipe-related, corrosion control and operational relevant factors to determine the Probability of Occurrence of SCC. Regression techniques were used to create multi-variable logistic regression models. The results for each model are checked at locations where SCC is known to be present or absent to assess predictive accuracy, then used to prioritize susceptible segments for field excavation. The relative strength of individual predictor variables provides insight into the mechanism of near-neutral pH SCC crack initiation.
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Rollin, Bertrand, and Malcolm J. Andrews. "On the “Early-Time” Evolution of Variables Relevant to Turbulence Models for the Rayleigh-Taylor Instability." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-30556.
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We present our progress toward setting initial conditions in variable density turbulence models. In particular, we concentrate our efforts on the BHR turbulence model [1] for turbulent Rayleigh-Taylor instability. Our approach is to predict profiles of relevant variables before fully turbulent regime and use them as initial conditions for the turbulence model. We use an idealized model of mixing between two interpenetrating fluids to define the initial profiles for the turbulence model variables. Velocities and volume fractions used in the idealized mixing model are obtained respectively from a set of ordinary differential equations modeling the growth of the Rayleigh-Taylor instability and from an idealization of the density profile in the mixing layer. A comparison between predicted profiles for the turbulence model variables and profiles of the variables obtained from low Atwood number three dimensional simulations show reasonable agreement.
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Zhang, Hui, Zhe Wang, Yan Li, Qi Zhang, and Jinchong Zhang. "Research on time relevant variables based fatigue level prediction model." In 2017 4th International Conference on Transportation Information and Safety (ICTIS). IEEE, 2017. http://dx.doi.org/10.1109/ictis.2017.8047712.
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López Guzmán, Juan Guillermo, and Cesar Julio Bustacara-Medina. "Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms." In WSCG'2021 - 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2021. Západočeská univerzita, 2021. http://dx.doi.org/10.24132/csrn.2021.3002.19.
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Popularity of MultiplayerOnlineBattle Arena (MOBA)video gameshas grown considerably, its popularity as well as the complexity of their playability, have attracted the attention in recent years of researchers from various areas of knowledge and in particular how they have resorted to different machine learning techniques. The papers reviewed mainly look for patterns in multidimensional data sets. Furthermore, these previous researches do not present a way to select the independent variables(predictors)to train the models. For this reason, this paper proposes a listof variables based on the techniques used and the objectives of the research. It allows to provide a set of variables to find patterns applied in MOBA videogames.In order to get the mentioned list,the consulted workswere groupedbythe used machine learning techniques, ranging from rule-based systems to complex neural network architectures. Also, a grouping technique is applied based on the objective of each research proposed.
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López Guzmán, Juan Guillermo, and Cesar Julio Bustacara Medina. "Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms." In WSCG'2021 - 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2021. Západočeská univerzita v Plzni, 2021. http://dx.doi.org/10.24132/csrn.2021.3101.19.
Full textAbstract:
Popularity of Multiplayer Online Battle Arena (MOBA) video games has grown considerably, its popularity as well as the complexity of their playability, have attracted the attention in recent years of researchers from various areas of knowledge and in particular how they have resorted to different machine learning techniques. The papers reviewed mainly look for patterns in multidimensional data sets. Furthermore, these previous researches do not present a way to select the independent variables (predictors) to train the models. For this reason, this paper proposes a list of variables based on the techniques used and the objectives of the research. It allows to provide a set of variables to find patterns applied in MOBA videogames. In order to get the mentioned list, the consulted works were grouped by the used machine learning techniques, ranging from rule-based systems to complex neural network architectures. Also, a grouping technique is applied based on the objective of each research proposed.
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Aqueveque, Pablo E., Eduardo P. Wiechmann, Javier Herrera, and Esteban Pino. "Measurable variables in copper Electrowinning and their relevance to predict process performance." In 2013 IEEE Industry Applications Society Annual Meeting. IEEE, 2013. http://dx.doi.org/10.1109/ias.2013.6682564.
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PFINGSTL, SIMON, CHRISTIAN BRAUN, and MARKUS ZIMMERMANN. "WARPED GAUSSIAN PROCESSES FOR PROGNOSTIC HEALTH MONITORING." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36358.
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Gaussian process regression is a powerful method for predicting states associated with uncertainty. A common application field is to predict damage states of structural systems. Recently, Gaussian processes became very popular as they deliver credible intervals for the predicted states. However, one major disadvantage of Gaussian processes is that they assume a normal distribution. This is not justified when the relevant variables can only assume positive values, such as crack lengths or damage states. This paper presents a way to bypass this problem by using warped Gaussian processes: We (1) transform the data with a warping function, (2) apply Gaussian process regression in the latent space, and (3) transform the results back by using the inverse of the warping function. The method is applied to a crack growth example. The paper shows how to integrate prior knowledge into warped Gaussian processes in order to increase prediction accuracy and that warped Gaussian processes lead to better and more plausible results.
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Reda Ali, Ahmed, Makky Sandra Jaya, and Ernest A. Jones. "Machine Learning Strategies for Accurate Log Prediction in Reservoir Characterization: Self-Calibrating Versus Domain-Knowledge." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205602-ms.
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Abstract Petrophysical evaluation is a crucial task for reservoir characterization but it is often complicated, time-consuming and associated with uncertainties. Moreover, this job is subjective and ambiguous depending on the petrophysicist's experience. Utilizing the flourishing Artificial Intelligence (AI)/Machine Learning (ML) is a way to build an automating process with minimal human intervention, improving consistency and efficiency of well log prediction and interpretation. Nowadays, the argument is whether AI-ML should base on a statistically self-calibrating or knowledge-based prediction framework! In this study, we develop a petrophysically knowledge-based AI-ML workflow that upscale sparsely-sampled core porosity and permeability into continuous curves along the entire well interval. AI-ML focuses on making predictions from analyzing data by learning and identifying patterns. The accuracy of the self-calibrating statistical models is heavily dependent on the volume of training data. The proposed AI-ML workflow uses raw well logs (gamma-ray, neutron and density) to predict porosity and permeability over the well interval using sparsely core data. The challenge in building the AI-ML model is the number of data points used for training showed an imbalance in the relative sampling of plugs, i.e. the number of core data (used as target variable) is less than 10%. Ensemble learning and stacking ML approaches are used to obtain maximum predictive performance of self-calibrating learning strategy. Alternatively, a new petrophysical workflow is established to debrief the domain experience in the feature selection that is used as an important weight in the regression problem. This helps ML model to learn more accurately by discovering hidden relationships between independent and target variables. This workflow is the inference engine of the AI-ML model to extract relevant domain-knowledge within the system that leads to more accurate predictions. The proposed knowledge-driven ML strategy achieved a prediction accuracy of R2 score = 87% (Correlation Coefficient (CC) of 96%). This is a significant improvement by R2 = 57% (CC = 62%) compared to the best performing self-calibrating ML models. The predicted properties are upscaled automatically to predict uncored intervals, improving data coverage and property population in reservoir models leading to the improvement of the model robustness. The high prediction accuracy demonstrates the potential of knowledge-driven AI-ML strategy in predicting rock properties under data sparsity and limitations and saving significant cost and time. This paper describes an AI-ML workflow that predicts high-resolution continuous porosity and permeability logs from imbalanced and sparse core plug data. The method successfully incorporates new type petrophysical facies weight as a feature augmentation engine for ML domain-knowledge framework. The workflow consisted of petrophysical treatment of raw data includes log quality control, preconditioning, processing, features augmentation and labelling, followed by feature selection to impersonate domain experience.
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Nalim, Razi, Hongwei Li, and Pezhman Akbari. "Air-Standard Aerothermodynamic Analysis of Gas Turbine Engines With Wave Rotor Combustion." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-60055.
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The wave rotor combustor can significantly improve gas turbine engine performance by implementing constant-volume combustion. The periodically open and closed combustor complicates thermodynamic analysis, and key cycle parameters depend on complex gas dynamics. In this study, a consistent air-standard aerothermodynamic model with variable specific heat is established. An algebraic model of the dominant gas dynamics estimates fill fraction and internal wave compression for typical port designs, using a relevant flow Mach number to represent wave amplitudes of compression and expansion. Nonlinear equations for thermodynamic state variables are solved numerically by Newton-Raphson iteration. Performance measures and key operating conditions are predicted, and a quasi-one-dimensional computational model is used to evaluate the usefulness of the algebraic model.
Reports on the topic "Relevant predictor variables"
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Valencia, Oscar, Juan José Díaz, and Diego A. Parra. Assessing Macro-Fiscal Risk for Latin American and Caribbean Countries. Inter-American Development Bank, November 2022. http://dx.doi.org/10.18235/0004530.
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This paper provides a comprehensive early warning system (EWS) that balances the classical signaling approach with the best-realized machine learning (ML) model for predicting fiscal stress episodes. Using accumulated local effects (ALE), we compute a set of thresholds for the most informative variables that drive the correlation between predictors. In addition, to evaluate the main country risks, we propose a leading fiscal risk indicator, highlighting macro, fiscal and institutional attributes. Estimates from different models suggest significant heterogeneity among the most critical variables in determining fiscal risk across countries. While macro variables have higher relevance for advanced countries, fiscal variables were more significant for Latin American and Caribbean (LAC) and emerging economies. These results are consistent under different liquidity-solvency metrics and have deepened since the global financial crisis.
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Or, Dani, Shmulik Friedman, and Jeanette Norton. Physical processes affecting microbial habitats and activity in unsaturated agricultural soils. United States Department of Agriculture, October 2002. http://dx.doi.org/10.32747/2002.7587239.bard.
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experimental methods for quantifying effects of water content and other dynamic environmental factors on bacterial growth in partially-saturated soils. Towards this end we reviewed critically the relevant scientific literature and performed theoretical and experimental studies of bacterial growth and activity in modeled, idealized and real unsaturated soils. The natural wetting-drying cycles common to agricultural soils affect water content and liquid organization resulting in fragmentation of aquatic habitats and limit hydraulic connections. Consequently, substrate diffusion pathways to soil microbial communities become limiting and reduce nutrient fluxes, microbial growth, and mobility. Key elements that govern the extent and manifestation of such ubiquitous interactions include characteristics of diffusion pathways and pore space, the timing, duration, and extent of environmental perturbations, the nature of microbiological adjustments (short-term and longterm), and spatial distribution and properties of EPS clusters (microcolonies). Of these key elements we have chosen to focus on a manageable subset namely on modeling microbial growth and coexistence on simple rough surfaces, and experiments on bacterial growth in variably saturated sand samples and columns. Our extensive review paper providing a definitive “snap-shot” of present scientific understanding of microbial behavior in unsaturated soils revealed a lack of modeling tools that are essential for enhanced predictability of microbial processes in soils. We therefore embarked on two pronged approach of development of simple microbial growth models based on diffusion-reaction principles to incorporate key controls for microbial activity in soils such as diffusion coefficients and temporal variations in soil water content (and related substrate diffusion rates), and development of new methodologies in support of experiments on microbial growth in simple and observable porous media under controlled water status conditions. Experimental efforts led to a series of microbial growth experiments in granular media under variable saturation and ambient conditions, and introduction of atomic force microscopy (AFM) and confocal scanning laser microscopy (CSLM) to study cell size, morphology and multi-cell arrangement at a high resolution from growth experiments in various porous media. The modeling efforts elucidated important links between unsaturated conditions and microbial coexistence which is believed to support the unparallel diversity found in soils. We examined the role of spatial and temporal variation in hydration conditions (such as exist in agricultural soils) on local growth rates and on interactions between two competing microbial species. Interestingly, the complexity of soil spaces and aquatic niches are necessary for supporting a rich microbial diversity and the wide array of microbial functions in unsaturated soils. This project supported collaboration between soil physicists and soil microbiologist that is absolutely essential for making progress in both disciplines. It provided a few basic tools (models, parameterization) for guiding future experiments and for gathering key information necessary for prediction of biological processes in agricultural soils. The project sparked a series of ongoing studies (at DTU and EPFL and in the ARO) into effects of soil hydration dynamics on microbial survival strategy under short term and prolonged desiccation (important for general scientific and agricultural applications).