Journal articles: 'Multiply regression'

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Chaurasia, Ashok, and Ofer Harel. "Using AIC in multiple linear regression framework with multiply imputed data." Health Services and Outcomes Research Methodology 12, no. 2-3 (2012): 219–33. http://dx.doi.org/10.1007/s10742-012-0088-8.

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Klein, Martin D., John Zylstra, and Bimal K. Sinha. "Finite Sample Inference for Multiply Imputed Synthetic Data under a Multiple Linear Regression Model." Calcutta Statistical Association Bulletin 71, no. 2 (2019): 63–82. http://dx.doi.org/10.1177/0008068318803814.

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In this article, we develop finite sample inference based on multiply imputed synthetic data generated under the multiple linear regression model. We consider two methods of generating the synthetic data, namely posterior predictive sampling and plug-in sampling. Simulation results are presented to confirm that the proposed methodology performs as the theory predicts and to numerically compare the proposed methodology with the current state-of-the-art procedures for analysing multiply imputed partially synthetic data. AMS 2000 subject classification: 62F10, 62F25, 62J05

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Golubova, T. N., N. M. Ovsyannikova, Z. R. Makhkamova, and I. Yu Tkachenko. "Multiply Regression Analysis as An Integrated Approach in the TB Indicators Assessment in the Republic of Crimea." Medicina 9, no. 2 (2021): 100–112. http://dx.doi.org/10.29234/2308-9113-2021-9-2-100-112.

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The use of multiply regression analysis for the TB indicators assessment and prediction in the Republic of Crimea is discussed. Basic TB indicators in the Republic of Crimea for the period 2014-2018 were studied using Pearson correlation coefficient and step-by-step multiply linear regression analysis. During paired correlation analysis, dependable association with an average and high degree is identified for the most indicators of interest. The multiple correlation coefficients were higher than the paired correlation coefficients, which demonstrated the greater significance of the group conditionality of the indicators. For key epidemiological TB indicators the regression equations are constructed and the most relevant predictors are found using stepwise multiply regression analysis. The regression equations for TB incidence, prevalence and mortality rates are proposed for practical applying to predict epidemiological situation.

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Li, Wei, Yuwen Gu, and Lan Liu. "Demystifying a class of multiply robust estimators." Biometrika 107, no. 4 (2020): 919–33. http://dx.doi.org/10.1093/biomet/asaa026.

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Summary For estimating the population mean of a response variable subject to ignorable missingness, a new class of methods, called multiply robust procedures, has been proposed. The advantage of multiply robust procedures over the traditional doubly robust methods is that they permit the use of multiple candidate models for both the propensity score and the outcome regression, and they are consistent if any one of the multiple models is correctly specified, a property termed multiple robustness. This paper shows that, somewhat surprisingly, multiply robust estimators are special cases of doubly robust estimators, where the final propensity score and outcome regression models are certain combinations of the candidate models. To further improve model specifications in the doubly robust estimators, we adapt a model mixing procedure as an alternative method for combining multiple candidate models. We show that multiple robustness and asymptotic normality can also be achieved by our mixing-based doubly robust estimator. Moreover, our estimator and its theoretical properties are not confined to parametric models. Numerical examples demonstrate that the proposed estimator is comparable to and can even outperform existing multiply robust estimators.

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Wang, Xiaorui, Guoyou Qin, Xinyuan Song, and Yanlin Tang. "Censored quantile regression based on multiply robust propensity scores." Statistical Methods in Medical Research 31, no. 3 (2021): 475–87. http://dx.doi.org/10.1177/09622802211060520.

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Censored quantile regression has elicited extensive research interest in recent years. One class of methods is based on an informative subset of a sample, selected via the propensity score. Propensity score can either be estimated using parametric methods, which poses the risk of misspecification or obtained using nonparametric approaches, which suffer from “curse of dimensionality.” In this study, we propose a new estimation method based on multiply robust propensity score for censored quantile regression. This method only requires one of the multiple candidate models for propensity score to be correctly specified, and thus, it provides a certain level of resistance to the misspecification of parametric models. Large sample properties, such as the consistency and asymptotic normality of the proposed estimator, are thoroughly investigated. Extensive simulation studies are conducted to assess the performance of the proposed estimator. The proposed method is also applied to a study on human immunodeficiency viruses.

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Sun, Yilun, Lu Wang, and Peisong Han. "Multiply robust estimation in nonparametric regression with missing data." Journal of Nonparametric Statistics 32, no. 1 (2019): 73–92. http://dx.doi.org/10.1080/10485252.2019.1700254.

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Han, Peisong. "Multiply Robust Estimation in Regression Analysis With Missing Data." Journal of the American Statistical Association 109, no. 507 (2014): 1159–73. http://dx.doi.org/10.1080/01621459.2014.880058.

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Lu, Wenqi, Guoyou Qin, Zhongyi Zhu, and Dongsheng Tu. "Multiply robust subgroup identification for longitudinal data with dropouts via median regression." Journal of Multivariate Analysis 181 (January 2021): 104691. http://dx.doi.org/10.1016/j.jmva.2020.104691.

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von Hippel, Paul T. "4. Regression with Missing Ys: An Improved Strategy for Analyzing Multiply Imputed Data." Sociological Methodology 37, no. 1 (2007): 83–117. http://dx.doi.org/10.1111/j.1467-9531.2007.00180.x.

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When fitting a generalized linear model—such as linear regression, logistic regression, or hierarchical linear modeling—analysts often wonder how to handle missing values of the dependent variable Y. If missing values have been filled in using multiple imputation, the usual advice is to use the imputed Y values in analysis. We show, however, that using imputed Ys can add needless noise to the estimates. Better estimates can usually be obtained using a modified strategy that we call multiple imputation, then deletion (MID). Under MID, all cases are used for imputation but, following imputation, cases with imputed Y values are excluded from the analysis. When there is something wrong with the imputed Y values, MID protects the estimates from the problematic imputations. And when the imputed Y values are acceptable, MID usually offers somewhat more efficient estimates than an ordinary MI strategy.

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Asyrofa, Rahmi, and Firdaus Mahmudy Wayan. "Regression Modelling for Precipitation Prediction Using Genetic Algorithms." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 3 (2017): 1290–300. https://doi.org/10.12928/TELKOMNIKA.v15i3.4028.

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This paper discusses the formation of an appropriate regression model in precipitation prediction. Precipitation prediction has a major influence to multiply the agricultural production of potatoes in Tengger, East Java, Indonesia. Periodically, the precipitation has non-linear patterns. By using a non-linear approach, the prediction of precipitation produces more accurate results. Genetic algorithm (GA) functioning chooses precipitation period which forms the best model. To prevent early convergence, testing the best combination value of crossover rate and mutation rate is done. To test the accuracy of the predicted results are used Root Mean Square Error (RMSE) as a benchmark. Based on the RMSE value of each method on every location, prediction using GA-Non-Linear Regression is better than Fuzzy Tsukamoto for each location. Compared to Generalized Space-Time Autoregressive-Seemingly Unrelated Regression (GSTAR-SUR), precipitation prediction using GA is better. This has been proved that for 3 locations GA is superior and on 1 location, GA has the least value of deviation level.

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van Ginkel, Joost R. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data." Psychometrika 85, no. 1 (2020): 185–205. http://dx.doi.org/10.1007/s11336-020-09696-4.

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Chaurasia, Ashok, and Ofer Harel. "PartialF-tests with multiply imputed data in the linear regression framework via coefficient of determination." Statistics in Medicine 34, no. 3 (2014): 432–43. http://dx.doi.org/10.1002/sim.6334.

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Hsu, Chiu-Hsieh, and Mandi Yu. "Cox regression analysis with missing covariates via nonparametric multiple imputation." Statistical Methods in Medical Research 28, no. 6 (2018): 1676–88. http://dx.doi.org/10.1177/0962280218772592.

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We consider the situation of estimating Cox regression in which some covariates are subject to missing, and there exists additional information (including observed event time, censoring indicator and fully observed covariates) which may be predictive of the missing covariates. We propose to use two working regression models: one for predicting the missing covariates and the other for predicting the missing probabilities. For each missing covariate observation, these two working models are used to define a nearest neighbor imputing set. This set is then used to non-parametrically impute covariate values for the missing observation. Upon the completion of imputation, Cox regression is performed on the multiply imputed datasets to estimate the regression coefficients. In a simulation study, we compare the nonparametric multiple imputation approach with the augmented inverse probability weighted (AIPW) method, which directly incorporates the two working models into estimation of Cox regression, and the predictive mean matching imputation (PMM) method. We show that all approaches can reduce bias due to non-ignorable missing mechanism. The proposed nonparametric imputation method is robust to mis-specification of either one of the two working models and robust to mis-specification of the link function of the two working models. In contrast, the PMM method is sensitive to misspecification of the covariates included in imputation. The AIPW method is sensitive to the selection probability. We apply the approaches to a breast cancer dataset from Surveillance, Epidemiology and End Results (SEER) Program.

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Grund, Simon, Oliver Lüdtke, and Alexander Robitzsch. "Pooling ANOVA Results From Multiply Imputed Datasets." Methodology 12, no. 3 (2016): 75–88. http://dx.doi.org/10.1027/1614-2241/a000111.

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Abstract. The analysis of variance (ANOVA) is frequently used to examine whether a number of groups differ on a variable of interest. The global hypothesis test of the ANOVA can be reformulated as a regression model in which all group differences are simultaneously tested against zero. Multiple imputation offers reliable and effective treatment of missing data; however, recommendations differ with regard to what procedures are suitable for pooling ANOVA results from multiply imputed datasets. In this article, we compared several procedures (known as D1, D2, and D3) using Monte Carlo simulations. Even though previous recommendations have advocated that D2 should be avoided in favor of D1 or D3, our results suggest that all procedures provide a suitable test of the ANOVA’s global null hypothesis in many plausible research scenarios. In more extreme settings, D1 was most reliable, whereas D2 and D3 suffered from different limitations. We provide guidelines on how the different methods can be applied in one- and two-factorial ANOVA designs and information about the conditions under which some procedures may perform better than others. Computer code is supplied for each method to be used in freely available statistical software.

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Roberts, John M., Aki Roberts, and Tim Wadsworth. "Multiple Imputation for Missing Values in Homicide Incident Data: An Evaluation Using Unique Test Data." Homicide Studies 22, no. 4 (2018): 391–409. http://dx.doi.org/10.1177/1088767918778309.

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Incident-level homicide datasets such as the Supplementary Homicide Reports (SHR) commonly exhibit missing data. We evaluated multiple imputation methods (that produce multiple completed datasets, across which imputed values may vary) via unique data that included actual values, from police agency incident reports, of seemingly missing SHR data. This permitted evaluation under a real, not assumed or simulated, missing data mechanism. We compared analytic results based on multiply imputed and actual data; multiple imputation rather successfully recovered victim–offender relationship distributions and regression coefficients that hold in the actual data. Results are encouraging for users of multiple imputation, though it is still important to minimize the extent of missing information in SHR and similar data.

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van Ginkel, Joost R. "Significance Tests and Estimates for R2 for Multiple Regression in Multiply Imputed Datasets: A Cautionary Note on Earlier Findings, and Alternative Solutions." Multivariate Behavioral Research 54, no. 4 (2019): 514–29. http://dx.doi.org/10.1080/00273171.2018.1540967.

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Wade, LJ, and JW Foreman. "Density multiply maturity interactions for grain yield in sunflower." Australian Journal of Experimental Agriculture 28, no. 5 (1988): 623. http://dx.doi.org/10.1071/ea9880623.

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Variation in grain yield response to density (10 000, 30000, 50000, 70000, 90 000 and 110 000 plants/ha), of 2 sunflower hybrids differing in maturity (Hysun 11 and Hysun 30), was examined over a range of yield levels provided by 9 locations. While the interaction between hybrid, density and site was statistically highly significant, its value for commercial recommendations was limited. Optimum yields by each hybrid at each yield level did not differ significantly from yields at densities of 50000 plants/ha. When combined with Victorian and southern Queensland data, the responses were consistent over sites, years and times of sowing. This suggests that valid agronomic recommendations over regions may be based on data for 1 region, providing a suitable data set covering the range of yield expectations is obtained. The regression technique, which related grain weight per plant to area per plant, enhanced the sensitivity in data analysis, simplified data presentation, and provided a framework for interpreting differences between hybrids in density response.

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Nozawa, T., Y. Yasumura, S. Futaki, et al. "Relation between oxygen consumption and pressure-volume area of in situ dog heart." American Journal of Physiology-Heart and Circulatory Physiology 253, no. 1 (1987): 1. http://dx.doi.org/10.1152/ajpheart.1987.253.1.1-a.

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Page H31: T. Nozawa, Y. Yasumura, S. Futaki, N. Tanaka, Y. Igarashi, Y. Goto, and H. Suga. “Relation between oxygen consumption and pressure-volume area of in situ dog heart.” Page H37: Fig. 6: Although the last sentence of the legend states “Solid lines are linear regression lines, and inner and outer pairs of dashed curves around them are 95% confidence limits of regression lines and data points, respectively,” these dashed lines show one standard deviation of both the slope of the regression line and the sampled data from the regression line. The authors forgot to multiply these standard deviation values by t value (2.069 for degrees of freedom = 23) for P le 0.05 in the computer software to obtain the 95% confidence limits. The other statistical results in Fig. 6 are correct.

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Prykhodko, S. B., I. S. Shutko, and A. S. Prykhodko. "A NONLINEAR REGRESSION MODEL TO ESTIMATE THE SIZE OF WEB APPS CREATED USING THE CAKEPHP FRAMEWORK." Radio Electronics, Computer Science, Control, no. 4 (January 13, 2022): 129–39. http://dx.doi.org/10.15588/1607-3274-2021-4-12.

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Context. The problem of estimating the software size in the early stage of a software project is important because a software size estimate is used for predicting the software development effort, including Web apps created using the CakePHP framework. The object of the study is the process of estimating the size of Web apps created using the CakePHP framework. The subject of the study is the nonlinear regression models to estimate the size of Web apps created using the CakePHP framework. Objective. The goal of the work is the building the nonlinear regression model with three predictors for estimating the size of Web apps created using the CakePHP framework on the basis of the Box-Cox four-variate normalizing transformation to increase the confidence in early size estimation of these apps. Method. The model, confidence and prediction intervals of multiply nonlinear regression to estimate the size of Web apps created using the CakePHP framework are constructed based on the Box-Cox multivariate normalizing transformation for non-Gaussian data with the help of appropriate techniques. The techniques to build the models, confidence, and prediction intervals of nonlinear regressions are based on the multiple nonlinear regression analysis using the multivariate normalizing transformations. The techniques allow taking into account the correlation between dependent and independent variables in the case of normalization of multivariate non-Gaussian data. In general, this leads to a reduction of the mean magnitude of relative error, the widths of the confidence, and prediction intervals in comparison with nonlinear models constructed using univariate normalizing transformations. Results. Comparison of the constructed model with the nonlinear regression models based on the decimal logarithm and the BoxCox univariate transformation has been performed. Conclusions. The nonlinear regression model with three predictors to estimate the size of Web apps created using the CakePHP framework is constructed on the basis of the Box-Cox four-variate transformation. This model, in comparison with other nonlinear regression models, has a larger multiple coefficient of determination, a smaller value of the mean magnitude of relative error and smaller widths of the confidence and prediction intervals. The prospects for further research may include the application of other multivariate normalizing transformations and data sets to construct the nonlinear regression model to estimate the size of Web apps created using the other frameworks.

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Hasanova, J., and S. Maxsudov. "AN ECONOMETRIC ANALYSIS OF THE IMPACT OF FLUCTUATING ELECTRICITY PRICES ON CONSUMER SPENDING AND INFLATION (CASE OF AZERBAIJAN)." Sciences of Europe, no. 137 (March 26, 2024): 20–25. https://doi.org/10.5281/zenodo.10874510.

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This article, based on data from 1995 to 2022, considers the impact of fluctuating electricity prices on consumer spending and inflation in Azerbaijan republic. In research have created multiply regression model and adequacy of the model was determined using the F-Fisher test, Student's t test, and the stability of the parameters of the regression model was checked using the CUSUM test. Stationarity of the time series was checked by the Dickey-Fuller test. As a result, econometrically sound recommendations are developed, which allows to conduct dynamic analyzes to effectively regulate economic process. The Granger causality of the relationship was investigated in research.

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Gandova, Vanya, Stanislava Tasheva, Krasimira Dobreva, Vanya Prodanova-Stefanova, Krasimira Marinova, and Albena Stoyanova. "Physicochemical, thermodynamic and thermal properties of linalyl acetate-ethanol-water system." E3S Web of Conferences 207 (2020): 01022. http://dx.doi.org/10.1051/e3sconf/202020701022.

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Some physicochemical, thermodynamic and thermal properties of linalyl acetate - ethanol - water solutions with different ethanol concentrations (70%, 75%, 80%, 85%, 90%, and 95%) in three ratios (1:5, 1:6, and 1:7) were determined. The properties were calculated to describe the stability of the system. Multiply linear regression model was obtained for surface tension prediction. Good correlation was observed between calculated and experimental surface tension values.

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Plaehn, Dave C., and David S. Lundahl. "Regression with multiple regressor arrays." Journal of Chemometrics 21, no. 12 (2007): 621–34. http://dx.doi.org/10.1002/cem.1092.

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Jalali, Maryam, Zahra Bagheri, Najaf Zare, and Seyyed Mohammad Taghi Ayatollahi. "A New Method for Imputing Censored Values in Crossover Designs with Time-to-Event Outcomes Using Median Residual Life." BioMed Research International 2020 (December 10, 2020): 1–8. http://dx.doi.org/10.1155/2020/8475154.

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Crossover designs are commonly applied in research due to efficiency and subject parsimony compared to parallel studies. Baseline measurements would improve the power of comparison. For time to event outcomes, the sample size is reduced due to censorship, if they are ignored; thus, applying traditional regression models will be limited. A logical solution is to impute the censored observation and apply common analytical models for analyzing the data. Nevertheless, techniques to impute censored data in time-to-event outcomes in crossover designs are not practiced as much. Accordingly, we propose a method to impute the censored observation using median residual life regression and then analyze the data using analyses of covariance (ANCOVA), considering the difference of period-specific baselines as covariate. We used simulation to show the favorable performance of our method relative to a recently proposed method, multiple imputation with model averaging and ANCOVA (MIMI). Specifically, the censored observations were multiply-imputed using prespecified parametric event time models, and then, the methods were applied to a real data example.

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Huang, Fan, Xiaolin Duan, Chengcong Hu, Mengce Zheng, and Honggang Hu. "MulLeak: Exploiting Multiply Instruction Leakage to Attack the Stack-optimized Kyber Implementation on Cortex-M4." IACR Transactions on Cryptographic Hardware and Embedded Systems 2025, no. 2 (2025): 23–68. https://doi.org/10.46586/tches.v2025.i2.23-68.

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CRYSTALS-Kyber, one of the NIST PQC standardization schemes, has garnered considerable attention from researchers in recent years for its side-channel security. Various targets have been explored in previous studies; however, research on extracting secret information from stack-optimized implementations targeting the Cortex-M4 remains scarce, primarily due to the lack of memory access operations, which increases the difficulty of attacks.This paper shifts the focus to the leakage of multiply instructions and present a novel cycle-level regression-based leakage model for the following attacks. We target the polynomial multiplications in decryption process of the stack-optimized implementation targeting the Cortex-M4, and propose two regression-based profiled attacks leveraging known ciphertext and chosen ciphertext methodologies to recover the secret coefficients individually. The later one can also be extended to the protected implementation.Our practical evaluation, conducted on the stack-optimized Kyber-768 implementation from the pqm4 repository, demonstrates the effectiveness of the proposed attacks. Focusing on the leakage from the pair-pointwise multiplication, specifically the macro doublebasemul_frombytes_asm, we successfully recover all secret coefficients with a success rate exceeding 95% using a modest number of traces for each attack. This research underscores the potential vulnerabilities in PQC implementations against side-channel attacks and contributes to the ongoing discourse on the physical security of cryptographic algorithms.

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Aßmann, Christian, Ariane Würbach, Solange Goßmann, Ferdinand Geissler, and Anika Bela. "Nonparametric Multiple Imputation for Questionnaires with Individual Skip Patterns and Constraints: The Case of Income Imputation in the National Educational Panel Study." Sociological Methods & Research 46, no. 4 (2015): 864–97. http://dx.doi.org/10.1177/0049124115610346.

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Large-scale surveys typically exhibit data structures characterized by rich mutual dependencies between surveyed variables and individual-specific skip patterns. Despite high efforts in fieldwork and questionnaire design, missing values inevitably occur. One approach for handling missing values is to provide multiply imputed data sets, thus enhancing the analytical potential of the surveyed data. To preserve possible nonlinear relationships among variables and incorporate skip patterns that make the full conditional distributions individual specific, we adapt a full conditional multiple imputation approach based on sequential classification and regression trees. Individual-specific skip patterns and constraints are handled within imputation in a way ensuring the consistency of the sequence of full conditional distributions. The suggested approach is illustrated in the context of income imputation in the adult cohort of the National Educational Panel Study.

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Kim, Soeun, Thomas R. Belin, and Catherine A. Sugar. "Multiple imputation with non-additively related variables: Joint-modeling and approximations." Statistical Methods in Medical Research 27, no. 6 (2016): 1683–94. http://dx.doi.org/10.1177/0962280216667763.

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This paper investigates multiple imputation methods for regression models with interacting continuous and binary predictors when continuous variable may be missing. Usual implementations for parametric multiple imputation assume a multivariate normal structure for the variables, which is not satisfied for a binary variable nor its interaction with a continuous variable. To accommodate interactions, missing covariates are multiply imputed from conditional distribution in a manner consistent with the joint model. Alternative imputation methods under multivariate normal assumptions are also considered as candidate approximations and evaluated in a simulation study. The results suggest that the joint modeling procedure performs generally well across a wide range of scenarios and so do the approximation methods that incorporate interactions in the model appropriately by stratification. It is critical to include interactions in the imputation model as failure to do so may result in low coverage and bias. We apply the joint modeling approach and approximation methods in the study of childhood trauma with gender × trauma interaction.

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Oktiani, Nurvi, Kartika Yuliantari, Taat Kuspriyono, Haryani Haryani, and Rani Kurniasari. "The Effect Promotion E- Commerce Toward Effectiveness Promotion By Using (AIDA) Methods." Widya Cipta: Jurnal Sekretari dan Manajemen 6, no. 1 (2022): 14–19. http://dx.doi.org/10.31294/widyacipta.v6i1.11683.

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Adpers-art communities is one of community have been built by similarity of hobbies, which is consist of making artworks in paper media or others which have three dimension. This research used quantitative method by multiply analysis regression, the sample from this research include 57 of consumer, the analysis data use reliability test, validity test, multicollinearity, Heteroscedasticity, Autocorrelation, normality, regression test. The Purpose of this research are known, the effect promotion E- Commerce toward effectiveness promotion by using AIDA Methods for Adpers – Art Community Product. For result, it can be described the regression model are significant for measuring percentage from dependent variable and change independent variable every 1%, coefficient regression from attention :3.28, interest 6.43, desire : 3.324 as much can make increase effectiveness promotion, and Action :3.309 as much give effect for increasing effectiveness promotion , in advice, Adpers Art Community must think of about how to improve attention, desire and action from consumer with any strategy for example increasing the effectiveness promotion by website and make specify strategies for improve especially attention, desire and action from consumer

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Swan, James H., Robert Friis, and Keith Turner. "Getting Tougher for the Fourth Quarter: Boomers and Physical Activity." Journal of Aging and Physical Activity 16, no. 3 (2008): 261–79. http://dx.doi.org/10.1123/japa.16.3.261.

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Baby Boomers might not consider themselves as growing old but are starting to reach the last quarter of average life spans. This article asks how Boomers prepare for their fourth quarters through physical activity. Three years (1999–2001) of National Health Interview Survey data yielded 96,501 adult respondents. Dependent variables were moderate, vigorous, and strengthening activity. Old boomers (1946–1955) and young boomers (1956–1965) were compared to respondents born before 1926, after 1975, and 10-year cohorts between. SUDAAN multiple logistic regression adjusted for complex sampling structure and multiply imputed income. Age-adjusted, older cohorts showed greater likelihood of activity than younger cohorts, offsetting moderate-activity declines with age until sharp decreases at advanced age: a plateau across Boomer and younger-aged cohorts. Interventions should promote activity at intensities and frequencies to which Boomers are most receptive.

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Plumpton, Catrin O., Tim Morris, Dyfrig A. Hughes, and Ian R. White. "Multiple imputation of multiple multi-item scales when a full imputation model is infeasible." BMC Research Notes 9, no. 1 (2016): 45. https://doi.org/10.1186/s13104-016-1853-5.

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Background: Missing data in a large scale survey presents major challenges. We focus on performing multiple imputation by chained equations when data contain multiple incomplete multi-item scales. Recent authors have proposed imputing such data at the level of the individual item, but this can lead to infeasibly large imputation models.Methods: We use data gathered from a large multinational survey, where analysis uses separate logistic regression models in each of nine country-specific data sets. In these data, applying multiple imputation by chained equations to the individual scale items is computationally infeasible. We propose an adaptation of multiple imputation by chained equations which imputes the individual scale items but reduces the number of variables in the imputation models by replacing most scale items with scale summary scores. We evaluate the feasibility of the proposed approach and compare it with a complete case analysis. We perform a simulation study to compare the proposed method with alternative approaches: we do this in a simplified setting to allow comparison with the full imputation model.Results: For the case study, the proposed approach reduces the size of the prediction models from 134 predictors to a maximum of 72 and makes multiple imputation by chained equations computationally feasible. Distributions of imputed data are seen to be consistent with observed data. Results from the regression analysis with multiple imputation are similar to, but more precise than, results for complete case analysis; for the same regression models a 39 % reduction in the standard error is observed. The simulation shows that our proposed method can perform comparably against the alternatives.Conclusions: By substantially reducing imputation model sizes, our adaptation makes multiple imputation feasible for large scale survey data with multiple multi-item scales. For the data considered, analysis of the multiply imputed data shows greater power and efficiency than complete case analysis. The adaptation of multiple imputation makes better use of available data and can yield substantively different results from simpler techniques.

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Lee, Kunyoung, Seunghyun Kim, and Eui Chul Lee. "Fast and Accurate Facial Expression Image Classification and Regression Method Based on Knowledge Distillation." Applied Sciences 13, no. 11 (2023): 6409. http://dx.doi.org/10.3390/app13116409.

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As emotional states are diverse, simply classifying them through discrete facial expressions has its limitations. Therefore, to create a facial expression recognition system for practical applications, not only must facial expressions be classified, emotional changes must be measured as continuous values. Based on the knowledge distillation structure and the teacher-bounded loss function, we propose a method to maximize the synergistic effect of jointly learning discrete and continuous emotional states of eight expression classes, valences, and arousal levels. The proposed knowledge distillation model uses Emonet, a state-of-the-art continuous estimation method, as the teacher model, and uses a lightweight network as the student model. It was confirmed that performance degradation can be minimized even though student models have multiply-accumulate operations of approximately 3.9 G and 0.3 G when using EfficientFormer and MobileNetV2, respectively, which is much less than the amount of computation required by the teacher model (16.99 G). Together with the significant improvements in computational efficiency (by 4.35 and 56.63 times using EfficientFormer and MobileNetV2, respectively), the decreases in facial expression classification accuracy were approximately 1.35% and 1.64%, respectively. Therefore, the proposed method is optimized for application-level interaction systems in terms of both the amount of computation required and the accuracy.

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Abdul Ameer Kadhum, Maha, and Entidhar Mhawes Zghair. "Implementation of Artificial Neural Network and Random Iteration Algorithm Uses in Medical Images." University of Thi-Qar Journal for Engineering Sciences 4, no. 1 (2013): 100–112. http://dx.doi.org/10.31663/utjes.v4i1.175.

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This research present a multiply connected neural network designed to estimate the fractal dimension (Df) using the Random Iteration Algorithm (IFSP). Fractal analysis is a powerful shape recognition tool and has been applied to many pattern recognition problems. Additionally, the one of the most adaptive Random Iteration Algorithm (IFSP) methods for estimating (Df). The architecture presented separates the calculation of (Df) into two sections, a data sampling section and a linear regression section. The data sampling section provides the ability to dyadic ally sample the data. The linear regression section simply calculates the slope of the best line through the sampling results. Instructional program was designed and built according to Knirk and Gustafson Design model, as one of the instructional design model in order to comprehension of the integrated ideas and concepts related to research .

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Ristianti, Komang Ardhelia, and Maria Mediatrix Ratna Sari. "Kesadaran Wajib Pajak, Kondisi Keuangan Wajib Pajak, Kebijakan Relaksasi Pajak, dan Kepatuhan Wajib Pajak." E-Jurnal Akuntansi 34, no. 3 (2024): 789. http://dx.doi.org/10.24843/eja.2024.v34.i03.p17.

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The purpose of this reserach is to determine the effect of taxpayer awareness, taxpayer financial condition, PKB whitening, and BBNKB exemption on motor vehicle taxpayer compliance in Gianyar Regency. The method of determining the sample using accidental sampling. 100 samples was calculated using slovin formula. Data collection was carried out through a survey method using a questionnaire. The analysis technique used multiply linier regression. The result indicate taxpayer awareness, taxpayer financial condition, PKB whitening, and BBNKB exemption have positife effect on motor vehicle taxpayer compilance in SAMSAT Office in Gianyar Regency.

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Sukarman, Sukarman. "ANALISIS KOMPETENSI PAMONG BELAJAR DI SANGGAR KEGIATAN BELAJAR (SKB) JAWA TENGAH." JIV 2, no. 1 (2007): 70–81. http://dx.doi.org/10.21009/jiv.0201.10.

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The purpose of this research is to know the correlation of the competence of learning mentors in District Learning Center and the contribution of education level, job experience, training, attitude to the profession and working climate toward the learning mentor’s competence. The population covers 328 learning mentors and the samples include 92 learning mentors from 11 out of 29. The data are collected using questionaires and analyzed using partial correlations and multiply regression techniques. The finding shows, signficant correlations betwen those four variables and the competence of the learning mentors.

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Shin, Changho, Sunghwan Joo, Jaeryun Yim, Hyoseop Lee, Taesup Moon, and Wonjong Rhee. "Subtask Gated Networks for Non-Intrusive Load Monitoring." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1150–57. http://dx.doi.org/10.1609/aaai.v33i01.33011150.

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Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household’s aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous household appliances can be avoided, and only one device needs to be installed. The problem has been well-known since Hart’s seminal paper in 1992, and recently significant performance improvements have been achieved by adopting deep networks. In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements. Specifically, we propose a subtask gated network that combines the main regression network with an on/off classification subtask network. Unlike typical multitask learning algorithms where multiple tasks simply share the network parameters to take advantage of the relevance among tasks, the subtask gated network multiply the main network’s regression output with the subtask’s classification probability. When standby-power is additionally learned, the proposed solution surpasses the state-of-the-art performance for most of the benchmark cases. The subtask gated network can be very effective for any problem that inherently has on/off states.

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Winkelmann, Marcel, Ada Luise Butz, Jan-Dierk Clausen, et al. "Admission blood glucose as a predictor of shock and mortality in multiply injured patients." SICOT-J 5 (2019): 17. http://dx.doi.org/10.1051/sicotj/2019015.

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Introduction: Reliable diagnosis of shock in multiply injured patients is still challenging in emergency care. Point-of-care tests could have the potential to improve shock diagnosis. Therefore, this study aimed to analyze the impact of admission blood glucose on predicting shock in multiply injured patients.Methods: A retrospective cohort analysis of patients with an injury severity score (ISS) ≥ 16 who were treated in a level I trauma center from 01/2005 to 12/2014 was performed. Shock was defined by systolic blood pressure ≤ 90 mmHg and/or shock index ≥ 0.9 at admission. Laboratory shock parameters including glucose were measured simultaneously. Receiver-operating-characteristic (ROC) analysis and multivariate logistic regression analysis was performed.Results: Seven hundred and seventy-two patients were analyzed of whom 93 patients (12.0%) died. Two hundred and fifty-nine patients (33.5%) were in shock at admission. Mortality was increased if shock was present at admission (18.1% vs. 9.0%,p < 0.001). Mean glucose was 9.6 ± 4.0 mmol/L if shock was present compared to 8.0 ± 3.0 mmol/L (p < 0.001). Admission glucose positively correlated with shock (Spearman rho = 0.2,p < 0.001). Glucose showed an AUC of 0.62 (95% CI [0.58–0.66],p < 0.001) with an optimal cut off value of 11.5 mmol/L. Patients with admission glucose of > 11.5 mmol/L had a 2.2-fold risk of shock (95% CI [1.4–3.4],p = 0.001). Admission blood glucose of > 11.5 mmol/L positively correlated with mortality too (Spearman rho = 0.65,p < 0.001). Patients had a 2.5-fold risk of dying (95% CI [1.3–4.8],p = 0.004).Discussion: Admission blood glucose was proven as an independent indicator of shock and mortality and, therefore, might help to identify multiply injured patients at particular risk.

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Kumar, Amit, Saurabh Mishra, and M. P. Sharma. "Assessment of ecological health of Baiyangdian lake in China using ecological health index." Journal of Applied and Natural Science 7, no. 2 (2015): 955–59. http://dx.doi.org/10.31018/jans.v7i2.713.

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An effective assessment of ecological health in aquatic ecosystems has become an important issue for researchers, policy-maker and environmentalist globally. The potential of thermodynamic oriented ecological indicators such as eco-exergy (Ex), structural eco-exergy (Exst), phytoplankton biomass (BA), and zooplankton biomass (ZA) in ecological assessment and management were used to calculate the ecological health and then correlated individually as well as multiply of Baiyangdian Lake located in the China. To establish a relationship between sub-EHI values of indicators to the overall EHI, data on thermodynamic indicators of the Baiyangdian lake were calculated from literature. The result indicates the ecological health of Baiyangdian lake is coming under the middle categories i.e. EHI= 40-60. A deeper analysis of the relationship between the thermodynamic parameters and EHI using mini tab software and the multiple regression revealed that the R2>0.9 for Baiyangdian Lake indicates that these correlations could be tentatively used to predict the ecological health of the Baiyangdian Lake in the future.

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Naomi, Prima, and Ayu Dwi Nindyati. "STUDI TENTANG ENTREPRENEURAL SELF EFFICACY DAN PROACTIVE PERSONALITY, SERTA PENGARUHNYA TERHADAP ENTREPRENEURAL INTENTION." Media Riset Bisnis & Manajemen 9, no. 3 (2009): 261–78. http://dx.doi.org/10.25105/mrbm.v9i3.1086.

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The purpose of this study was to know the grade level of entrepreneural intention among Paramadina's student, and to investigate the affecting role of Entrepreneurial Self Efficacy and Proactive Personality to Entrepreneural Intention, The subject of the study is the Paramadina Student who has taken entrepereneurship subject. The author used proportional cluster sampling and analyze done by decscriptive statistics and multiple regression analysis.The results showed that entrepreneurial intention among students is moderately high and all of variable of this research were significantly affecting to Entrepreneural Intention. The results strenghthen the previous research, that has proven both practical and theoretical implication. The theoretical implication showed that needed to investigate the proactive personality variable within other research subject and consider to use of moderate variable which can multiply the effect of proactive personalty toward entrepreneurial intention. The practical implication was suggested by applying the learning process toward developing entrepreneur self efficacy.Keywords : Entrepreneural intention, Entrepreneur& self efficacy, Proactive personality

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Jones, Philip H., Jeffrey L. Van Zant, and F. Stephen Dobson. "Variation in reproductive success of male and female Columbian ground squirrels (Urocitellus columbianus)." Canadian Journal of Zoology 90, no. 6 (2012): 736–43. http://dx.doi.org/10.1139/z2012-042.

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The imbalanced reproductive success of polygynous mammals results in sexual selection on male traits like body size. Males and females might have more balanced reproductive success under polygynandry, where both sexes mate multiply. Using 4 years of microsatellite DNA analyses of paternity and known maternity, we investigated variation in reproductive success of Columbian ground squirrels, Urocitellus columbianus (Ord, 1815); a species with multiple mating by both sexes and multiple paternity of litters. We asked whether male reproductive success was more variable than that of females under this mating system. The overall percentage of confirmed paternity was 61.4% of 339 offspring. The mean rate of multiple paternity in litters with known fathers was 72.4% (n = 29 litters). Estimated mean reproductive success of males (10.27 offspring) was about thrice that of females (3.11 offspring). Even after this difference was taken into account statistically, males were about three times as variable in reproductive success as females (coefficients of variation = 77.84% and 26.74%, respectively). The Bateman gradient (regression slope of offspring production on number of successful mates) was significantly greater for males (βM = 1.44) than females (βF = 0.28). Thus, under a polygynandrous mating system, males exhibited greater variation in reproductive success than females.

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Yu, Huanglin, Ke Chen, Kaiqi Wang, Yanlin Qian, Zhaoxiang Zhang, and Kui Jia. "Cascading Convolutional Color Constancy." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12725–32. http://dx.doi.org/10.1609/aaai.v34i07.6966.

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Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy. However, it's still challenging due to intrinsic appearance and label ambiguities caused by unknown illuminants, diverse reflection properties of materials and extrinsic imaging factors (such as different camera sensors). In this paper, we introduce a novel algorithm – Cascading Convolutional Color Constancy (in short, C4) to improve robustness of regression learning and achieve stable generalization capability across datasets (different cameras and scenes) in a unique framework. The proposed C4 method ensembles a series of dependent illumination hypotheses from each cascade stage via introducing a weighted multiply-accumulate loss function, which can inherently capture different modes of illuminations and explicitly enforce coarse-to-fine network optimization. Experimental results on the public Color Checker and NUS 8-Camera benchmarks demonstrate superior performance of the proposed algorithm in comparison with the state-of-the-art methods, especially for more difficult scenes.

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Sajdak, Marcin, and Łukasz Smędowski. "Application of multivariate data analysis in the construction of predictive model for the chemical properties of coke." Contemporary Trends in Geoscience 2, no. 1 (2013): 67–74. http://dx.doi.org/10.2478/ctg-2014-0010.

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ABSTRACT The aim of this work was to develop a statistical model which can predict values describing chemical composition of cokes performed in industrial scale. This model was developed on the basis of data that were taken from the production system used in the one of Polish coking plant. Elaborated equation include quality parameters of initial coals that form coal blends as well as contribution of additions such as coke and petrochemical coke. These equations allow to predict chemical composition of coke, e.g. contributions of: sulphur, ash, phosphorus and chlorine within the coke. A model was elaborated with use of STATISTICA 10 program and it is based on factor and multiply regression analyses. These analyses were chosen from among few kinds of regression analyses. They allowed to develop prediction model with the required goodness of fit between calculated and actual values. Goodness of fit was elaborated with: • residuals analyses, • residues normality and predicted normality • mean absolute error • Pearson correlation confidence

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Anigbogu, Theresa Ukamaka, and Scholastica Okeke Chikodiri. "Determinants of Savings Among Members of Cooperative Societies in Anambra State, Nigeria." International Journal of Trend in Scientific Research and Development 3, no. 1 (2018): 752–59. https://doi.org/10.31142/ijtsrd19075.

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This study examines the determinants of savings among members of cooperative societies in Anambra state. Specifically, it provides empirical evidence on the socio economic characteristics of members of the co operatives and ascertains which of the socio economic characteristics significantly determine savings mobilization among members of the cooperative groups. It also ascertained the range of savings of the members of the cooperative groups and identified the cooperative members' reasons for saving. Data for the study were obtained from 100 cooperative members with the aid of well structured questionnaires through a simple random sampling technique. Data were analyzed using descriptive statistics and multiple regression analysis. Results obtained showed that on the average the members saved N12, 241.57 every month. The average monthly savings is encouraging considering the fact that their monthly income is low. The major reasons for saving as indicated by the respondents include Security, Statutory as Cooperative Member, Investment and to obtain Loans. This is obtained from their mean statistics of 4.00, 3.87, 3.53 and 3.00 respectively. The R2 value of 0.916 obtains indicates that about 91.6 of observed variation in savings by farmers could be attributed to the combined influence of the various independent variables included in the regression equation. The F statistic with 95.342 was significant at 0.000 levels of significance. There is a significant variation in the range of savings of the members of the cooperative groups. Socio economic characteristics of members significantly determine the savings of members of cooperative societies in the state. Based on the analysis and findings of this study, the researcher therefore recommends that To increase the farmers' savings potentials, saving should be made statutory as cooperative member. The members should also be encouraged to invest more no matter how small. It is good to save but members should have predefined reasons before saving to enable them make judicious use of whatever amount saved. There is the need to improve the livelihood strategies of the farmers to bridge the noticeable gap that exist in the farmers' savings range. Anigbogu, Theresa Ukamaka | , Chikodiri Scholastica | Okeke, Uju M "Determinants of Savings Among Members of Cooperative Societies in Anambra State, Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19075.pdf

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Tosto, Giuseppe, Thomas D. Bird, Debby Tsuang, et al. "Polygenic risk scores in familial Alzheimer disease." Neurology 88, no. 12 (2017): 1180–86. http://dx.doi.org/10.1212/wnl.0000000000003734.

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Objective:To investigate the association between a genetic risk score (GRS) and familial late-onset Alzheimer disease (LOAD) and its predictive value in families multiply affected by the disease.Methods:Using data from the National Institute on Aging Genetics Initiative for Late-Onset Alzheimer Disease (National Institute on Aging–Late-Onset Alzheimer's Disease Family Study), mixed regression models tested the association of familial LOAD with a GRS based on single nucleotide polymorphisms (SNPs) previously associated with LOAD. We modeled associations using unweighted and weighted scores with estimates derived from the literature. In secondary models, we adjusted subsequent models for presence of the APOE ε4 allele and further tested the interaction between APOE ε4 and the GRS. We constructed a similar GRS in a cohort of Caribbean Hispanic families multiply affected by LOAD by selecting the SNP with the strongest p value within the same regions.Results:In the NIA-LOAD families, the GRS was significantly associated with LOAD (odds ratio [OR] 1.29; 95% confidence interval 1.21–1.37). The results did not change after adjusting for APOE ε4. In Caribbean Hispanic families, the GRS also significantly predicted LOAD (OR 1.73; 1.57–1.93). Higher scores were associated with lower age at onset in both cohorts.Conclusions:High GRS increases the risk of familial LOAD and lowers the age at onset, regardless of ethnic group.

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Nailin Nishfi Fajriyah. "Pengaruh Metode An-Nashr Terhadap Perbendaharaan Kosakata Bahasa Arab Di Madrasah Diniyah Takwiliyah Awaliyah Hidayatulloh Bangsal Mojokerto." Impressive: Journal of Education 1, no. 2 (2023): 82–93. https://doi.org/10.61502/ijoe.v1i2.14.

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Arabic is a foreign language for foreign speakers who have a mother tongue other than Arabic. As a foreign speaker, to expand the understanding of Arabic is to multiply the mastery of each meaning of the Arabic vocabulary. In this case, to multiply the vocabulary treasury is to use the an-Nashr method. The purpose of this research are: 1) to determine the effect of the an-Nasr method on Arabic vocabulary at Madrasah Diniyah Takwiliyah Awaliyah Hidayatulloh Bangsal Mojokerto. 2) to find out the obstacles and supporters of the an-Nasr method on Arabic vocabulary at Madrasah Diniyah Takwiliyah Awaliyah Hidayatulloh Bangsal Mojokerto. In conducting this research, the researcher used a descriptive quantitative approach using research instruments in the form of questionnaires, interviews, documentation, and observations. The data were analyzed with the coefficient of determination and simple linear regression, presented the data, and drew conclusions. The results of this study are: 1) Arabic vocabulary acquired by students increases by 72.7%. 2) The results of this study indicate that there is a positive effect of the an-Nasr method on Arabic vocabulary with a significant level of 0.001. So it can be concluded that Ho is rejected and Ha is accepted.

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Lachin, John M. "Sample size evaluation for a multiply matched case–control study using the score test from a conditional logistic (discrete Cox PH) regression model." Statistics in Medicine 27, no. 14 (2008): 2509–23. http://dx.doi.org/10.1002/sim.3057.

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Lachin, John M. "Sample size evaluation for a multiply matched case-control study using the score test from a conditional logistic (discrete Cox PH) regression model." Statistics in Medicine 37, no. 10 (2018): 1765–66. http://dx.doi.org/10.1002/sim.7598.

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Emsell, L., C. Chaddock, N. Forde, et al. "White matter microstructural abnormalities in families multiply affected with bipolar I disorder: a diffusion tensor tractography study." Psychological Medicine 44, no. 10 (2013): 2139–50. http://dx.doi.org/10.1017/s0033291713002845.

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BackgroundWhite matter (WM) abnormalities are proposed as potential endophenotypic markers of bipolar disorder (BD). In a diffusion tensor imaging (DTI) voxel-based analysis (VBA) study of families multiply affected with BD, we previously reported that widespread abnormalities of fractional anisotropy (FA) are associated with both BD and genetic liability for illness. In the present study, we further investigated the endophenotypic potential of WM abnormalities by applying DTI tractography to specifically investigate tracts implicated in the pathophysiology of BD.MethodDiffusion magnetic resonance imaging (MRI) data were acquired from 19 patients with BD type I from multiply affected families, 21 of their unaffected first-degree relatives and 18 healthy volunteers. DTI tractography was used to identify the cingulum, uncinate fasciculus (UF), arcuate portion of the superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), corpus callosum, and the anterior limb of the internal capsule (ALIC). Regression analyses were conducted to investigate the effect of participant group and genetic liability on FA and radial diffusivity (RD) in each tract.ResultsWe detected a significant effect of group on both FA and RD in the cingulum, SLF, callosal splenium and ILF driven by reduced FA and increased RD in patients compared to controls and relatives. Increasing genetic liability was associated with decreased FA and increased RD in the UF, and decreased FA in the SLF, among patients.ConclusionsWM microstructural abnormalities in limbic, temporal and callosal pathways represent microstructural abnormalities associated with BD whereas alterations in the SLF and UF may represent potential markers of endophenotypic risk.

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Tsung-Hao CHEN, Tsung-Hao CHEN, Yu-Lun SUN Tsung-Hao CHEN, Daniel Chan-wei TSAI Yu-Lun SUN, Yi-Mei HUANG Daniel Chan-wei TSAI, and Shijun ZHANG Yi-Mei HUANG. "Diagnosing and Mitigating Multicollinearity in Moderated Multiple Regression." 青年企業管理評論 17, no. 1 (2024): 009–22. http://dx.doi.org/10.53106/207308882024101701002.

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This paper primarily explores the challenges associated with Moderated Multiple Regression Models, particularly how a moderator variable (m) influences the direction or strength of the relationship between an independent variable (x) and a dependent variable (Y). A significant issue arises when there is a high correlation between the independent variable and the moderator, leading to severe multicollinearity. That complicates the accurate estimation of the independent variables’ effects on the dependent variable (Myers, 1990). We develop five moderated multiple regression models with purpose of mitigating the multicollinearity in the analysis. Our empirical findings indicate that three of them perform good tested by the variance inflation factor and condition index. We finally suggest a process of standardizing both independent variable and moderator and taking the cross multiplication by those two standardized variables before conducting moderated multiple regression analysis. &nbsp;

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Kotecha, Rupesh, Nicholas Damico, Jacob A. Miller, et al. "Three or More Courses of Stereotactic Radiosurgery for Patients with Multiply Recurrent Brain Metastases." Neurosurgery 80, no. 6 (2017): 871–79. http://dx.doi.org/10.1093/neuros/nyw147.

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Abstract BACKGROUND: Although patients with brain metastasis are treated with primary stereotactic radiosurgery (SRS), the use of salvage therapies and their consequence remains understudied. OBJECTIVE: To study the intracranial recurrence patterns and salvage therapies for patients who underwent multiple SRS courses. METHODS: A retrospective review was performed of 59 patients with brain metastases who underwent ≥3 SRS courses for new lesions. Cox regression analyzed factors predictive for overall survival. RESULTS: The median age at diagnosis was 52 years. Over time, patients underwent a median of 3 courses of SRS (range: 3-8) to a total of 765 different brain metastases. The 6-month risk of distant intracranial recurrence after the first SRS treatment was 64% (95% confidence interval: 52%-77%). Overall survival was 40% (95% confidence interval: 28%-53%) at 24 months. Only 24 patients (41%) had a decline in their Karnofsky Performance Status ≤70 at last office visit. Quality of life was preserved among 77% of patients at 12 months, with 45% experiencing clinically significant improvement during clinical follow-up. Radiation necrosis developed in 10 patients (17%). On multivariate analysis, gender (males, Hazard Ratio [HR]: 2.0, P &lt; .05), Karnofsky Performance Status ≤80 (HR 3.2, P &lt; .001), extracranial metastases (HR: 3.6, P &lt; .001), and a distant intracranial recurrence ≤3 months from initial to repeat SRS (HR: 3.8, P &lt; .001) were associated with a poorer survival. CONCLUSION: In selected patients, performing ≥3 SRS courses controls intracranial disease. Patients may need salvage SRS for distant intracranial relapse, but focal retreatments are associated with modest toxicity, do not appear to negatively affect a patient's performance status, and help preserve quality of life.

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Gandova, Vanya, Stanislava Tasheva, Vanya Prodanova-Stefanova, Krasimira Dobreva, Ivayla Dincheva, and Albena Stoyanova. "Investigation of the thermodynamic and physicochemical properties of lavender oil (Lavandula angustifolia Mill.)-ethanol-water system." E3S Web of Conferences 286 (2021): 02004. http://dx.doi.org/10.1051/e3sconf/202128602004.

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The thermodynamic and physicochemical properties of lavender oil (Lavandula angustifolia Mill.)-ethanol-water system were measured. The chemical composition of solutions of the lavender oil in 70%, 75%, 80%, 85%, 90%, and 95% ethanol was determined using GC/MS analysis. The main compounds in the solutions are oxygenated monoterpenes (β-linalool, terpinen-4-ol, α-terpineol, and linalyl acetate), followed by monoterpene hydrocarbons ((Z)-β-ocimene and (E)-β-ocimene), and sesquiterpene hydrocarbons (β-caryophyllene and (E)-β-farnesene). The ternary phase diagrams were constructed. By multiply linear regression model for surface tension prediction was applied. The thermodynamic and kinetic parameters of lavender oil – ethanol - water system were calculated (energy of Gibb’s, enthalpy, entropy, energy of activation, and coefficient of diffusion).

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Iswati, Sri. "PENGARUH KOMITMEN PROFESIONAL, TIPE KEPRIBADIAN, GENDER TERHADAP KEPUASAN KERJA AKUNTAN PUBLIK." EKUITAS (Jurnal Ekonomi dan Keuangan) 12, no. 1 (2017): 37. http://dx.doi.org/10.24034/j25485024.y2008.v12.i1.2058.

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The objective of the research is to test professional commitment affects to the job satisfaction of public accountant by means of the personality and gender as moderator variables. This is a quantitative research that uses multiply regression analysis. Primary data are gathered through questionnaires and executed by reliability and validity test.And the results show that the six hypotheses have proved significantly: (1) the professional commitment affect the job satisfaction; (2) personalit affect to the job satisfaction; (3) the gender do not affect to the job satisfaction; (4) the interaction between professional commitment and personality affect the job satisfaction; (5) the interaction between professional commitment and gender affect to the job satisfaction; and (6) all variables affect to the job satisfaction of public accountant.

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Journal articles on the topic 'Multiply regression'

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