Coefficient Regression Model Research Articles

Power Analysis for Parameter Estimation in Structural Equation Modeling: A Discussion and Tutorial

Despite the widespread and rising popularity of structural equation modeling (SEM) in psychology, there is still much confusion surrounding how to choose an appropriate sample size for SEM. Currently available guidance primarily consists of sample-size rules of thumb that are not backed up by research and power analyses for detecting model misspecification. Missing from most current practices is power analysis for detecting a target effect (e.g., a regression coefficient between latent variables). In this article, we (a) distinguish power to detect model misspecification from power to detect a target effect, (b) report the results of a simulation study on power to detect a target regression coefficient in a three-predictor latent regression model, and (c) introduce a user-friendly Shiny app, pwrSEM, for conducting power analysis for detecting target effects in structural equation models.

Computation of Composite Mg II Core-to-Wing Ratio for Solar Cycle 22 and 23

The Mg II core-to-wing ratio (c/w ratio) data is used to derive the solar extreme UV emission which brings a vital role in the creation of the Earth’s ionosphere region. A continuous Mg II c/w ratio data has scientific importance towards the analysis of the earth’s climate as well as for the solar chromosphere region. Various instruments of ESA and NASA satellites such as SUSIM, SOLSTICE, GOME, NOAA9, NOAA11 etc. measure Mg II c/w ratio data at different time scales but still some missing measurements are being reported. To fill those missing gaps, the current investigation has used the correlation analysis technique between Mg II and other solar indices separately. Our result establishes a highest correlation with 10.7 cm (2800 MHz) radio flux (F10.7) data in comparison to other solar indices during solar cycle 22 and 23. But for phase-wise correlation analysis, the correlation coefficient shows varying behavior during declining and rising phase of each solar cycle due to hysteresis of magnetic field inside the Sun. Also, the correlation coefficient of the linear regression model for Coronal Index vs. Mg II has a higher value than 10.7 cm radio flux vs. Mg II during maximum phase of each solar cycle. In the present investigation, the composite Mg II c/w ratio data is computed using a linear regression model with strongly correlated solar index data depending on time frame analysis of each solar cycle to achieve better correlation. Finally, a correlation analysis is also being performed between the computed composite Mg II c/w ratio and Bremen composite data and a fair correlation around 0.98 has been found. The computed series is also validated with other long-range solar indices data to verify different long-range trends that can reflect the actual nature of the Sun’s chromosphere region.

Inference on semiparametric multinomial response models

We explore inference on regression coefficients in semiparametric multinomial response models. We consider cross‐sectional, and both static and dynamic panel settings where we focus throughout on inference under sufficient conditions for point identification. The approach to identification uses a matching insight throughout all three models coupled with variation in regressors: with cross‐section data, we match across individuals while with panel data, we match within individuals over time. Across models, we relax the Indpendence of Irrelevant Alternatives (or IIA assumption, see McFadden (1974)) and allow for arbitrary correlation in the unobservables that determine utility of various alternatives. For the cross‐sectional model, estimation is based on a localized rank objective function, analogous to that used in Abrevaya, Hausman, and Khan (2010), and presents a generalization of existing approaches. In panel data settings, rates of convergence are shown to exhibit a curse of dimensionality in the number of alternatives. The results for the dynamic panel data model generalize the work of Honoré and Kyriazidou (2000) to cover the semiparametric multinomial case. A simulation study establishes adequate finite sample properties of our new procedures. We apply our estimators to a scanner panel data set.

Additive-Multiplicative Rates Model for Recurrent Event Data with Intermittently Observed Time-Dependent Covariates.

Regression methods, including the proportional rates model and additive rates model, have been proposed to evaluate the effect of covariates on the risk of recurrent events. These two models have different assumptions on the form of the covariate effects. A more flexible model, the additive-multiplicative rates model, is considered to allow the covariates to have both additive and multiplicative effects on the marginal rate of recurrent event process. However, its use is limited to the cases where the time-dependent covariates are monitored continuously throughout the follow-up time. In practice, time-dependent covariates are often only measured intermittently, which renders the current estimation method for the additive-multiplicative rates model inapplicable. In this paper, we propose a semiparametric estimator for the regression coefficients of the additive-multiplicative rates model to allow intermittently observed time-dependent covariates. We present the simulation results for the comparison between the proposed method and the simple methods, including last covariate carried forward and linear interpolation, and apply the proposed method to an epidemiologic study aiming to evaluate the effect of time-varying streptococcal infections on the risk of pharyngitis among school children. The R package implementing the proposed method is available at www.github.com/TianmengL/rectime.

The correlation between external apical root resorption and electric pulp test responses: a prospective clinical trial.

ABSTRACTObjective: The current study investigated the correlation between pulpal sensitivity to the electric pulp tester (EPT) and external apical root resorption (EARR) in four types of maxillary anterior teeth of fixed orthodontic treatment patients. Methods: In this prospective cohort study, 232 anterior teeth of 58 patients (mean age 18.96 ± 6.13 years) treated with fixed orthodontic treatment were examined. The EPT readings were recorded at twelve time points immediately before archwire insertion. Root resorption of four maxillary incisors were measured by means of parallel periapical radiographs at three time intervals (six months interval from the start) through design-to-purpose software to optimize data collection. A multiple linear regression model and Pearson correlation coefficient were used to assess the association of EPT values and observed EARR (p< 0.05).Results: The highest level of EPT measurement was recorded at initial visit, and then there was a decreasing trend in EPT level during treatment for the next six and twelve months. There was another increasing trend after six months till the finishing time of the treatment. There was a significant correlation between changes in root length and time of recording the root length (p< 0.001). There was significant positive correlation between changes in EPT level and amount of observed root resorption (p< 0.001).Conclusion: The relative decrease in electric pulp test level could be a diagnostic sign of root resorption during orthodontic treatment. Further studies with longer follow up are needed to confirm the current results.

Ekonomski instrumenti u oblasti zaštite životne sredine u Republici Srbiji

In order for the processes of environment protection and improvement in the Republic of Serbia to be more efficient, certain financial investments are necessary. After an introductory overview, the aim of the research was presented, which is to show correlation between costs and tax revenues in the field of environmental protection. The linear regression model and Pearson's correlation coefficient were used for the analyses. The paper presents economic instruments in the field of environmental protection for the period 2009-2018. Data obtained from the National Statistical Office were used. The state of the environment, as well as the goals related to European integration with the opening of Chapter 27, show that the presented data relating to the environment in the Republic of Serbia are very worrying and represent one of the key problems for policy implementation. The obtained results showed that there is a significant degree of correlation (r = 0.887). The current situation indicates that special attention should be paid to this topic, especially in terms of finding mechanisms for providing the necessary financial funds.

Regularized Ordinal Regression and the ordinalNet R Package.

Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each model in this class generalizes to a more flexible form, that can be used to model either ordered or unordered categorical response data. We call this the elementwise link multinomial-ordinal (ELMO) class, and it includes widely used models such as multinomial logistic regression (which also has an ordinal form) and ordinal logistic regression (which also has an unordered multinomial form). We introduce an elastic net penalty class that applies to either model form, and additionally, this penalty can be used to shrink a non-ordinal model toward its ordinal counterpart. Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class.

A multiple linear regression model with multiplicative log-normal error term for atmospheric concentration data

The homoscedasticity assumption (the variance of the error term is the same across all the observations) is a key assumption in the ordinary linear squares (OLS) solution of a linear regression model. The validity of this assumption is examined for a multiple linear regression model used to determine the source contributions to the observed black carbon concentrations at 12 background monitoring sites across China using a hybrid modeling approach. Residual analysis from the traditional OLS method, which assumes that the error term is additive and normally distributed with a mean of zero, shows pronounced heteroscedasticity based on the Breusch–Pagan test for 11 datasets. Noticing that the atmospheric black carbon data are log-normally distributed, we make a new assumption that the error terms are multiplicative and log-normally distributed. When the coefficients of the multilinear regression model are determined using the maximum likelihood estimation (MLE), the distribution of the residuals in 8 out of the 12 datasets is in good accordance with the revised assumption. Furthermore, the MLE computation under this novel assumption could be proved mathematically identical to minimizing a log-scale objective function, which considerably reduces the complexity in the MLE calculation.The new method is further demonstrated to have clear advantages in numerical simulation experiments of a 5-variable multiple linear regression model using synthesized data with prescribed coefficients and lognormally distributed multiplicative errors. Under all 9 simulation scenarios, the new method yields the most accurate estimations of the regression coefficients and has significantly higher coverage probability (on average, 95% for all five coefficients) than OLS (79%) and weighted least squares (WLS, 72%) methods.

Modelos de regresión segmentada para estimar el tamaño óptimo de la parcela experimental con caña de azúcar

El objetivo de esta investigación fue estimar el tamaño óptimo de parcela experimental con caña de azúcar en la Región Brunca de Costa Rica. Se utilizaron modelos de regresión segmentada con los datos obtenidos de un ensayo de uniformidad (40 surcos de 84 metros de largo con una separación entre cada uno de 1.5 metros, para un total de 5 040 m2 de área experimental), el trabajo de campo se realizó en la finca El Porvenir en Pérez Zeledón Costa Rica, entre el año 2018 y 2019. Los coeficientes de los modelos de regresión lineal con constante (LRP) y de regresión cuadrática con constante (QRP) fueron estadísticamente significativos. Se concluyó que el tamaño óptimo de parcela que minimiza el error experimental para los ensayos que se establezcan en la región, deberían estar en el rango de 72 a 93 m2.

The multiple linear regression models of heat load for air-conditioned room

This study developed a heat load model of an air-conditioned room using multiple linear regression. A sensitivity analysis was first used to simplify the heat load model by neglecting the less sensitive levels of independent variables. The experiments were tested in three thermostat setting conditions of 25, 26, and 27 °C. The experimental data were collected to determine normalized sensitivity coefficients and regression coefficients of the heat load model. The sensitivity analysis results showed that the radiation parameters had a major impact on heat load. Furthermore, the air-conditioned room properties did not give a significant difference in the heat load as compared to other parameters. Moreover, the heat load model with the regression coefficients was reported, as well as the correlation coefficient. This model gave a good prediction of the heat load with an error of 0.15%.

Positive Definiteness of the Asymptotic Covariance Matrix of OLS Estimators in Parsimonious Regressions; Comments on ’Testing a Large Set of Zero Restrictions in Regression Models, With an Application to Mixed Frequency Granger Causality’ by Ghysels, Hill, and Motegi (2020)

Recently, Ghysels, Hill, and Motegi (2020) have proposed a test for a large set of zero restrictions on coefficients in regression models. They referred to the test as a ``max test''. The test statistic for the max test is calculated by first running OLS regressions, each of which includes only one of explanatory variables whose coefficients are under examination, and then taking the maximum value of the squared OLS estimates of those coefficients. They called those regressions ``parsimonious regressions''. In this paper, we answer a question raised in Remark 2.4 in Ghysels, Hill, and Motegi (2020), namely, whether the asymptotic covariance matrix of the OLS estimators in the parsimonious regressions is, in general, positive definite. We show that it is generally positive definite. The result may be utilized to facilitate the calculation of the simulated p values necessary for implementing the max test.

Prediction of wine sensory properties using mid-infrared spectra of Cabernet Sauvignon and Chardonnay grape berries and wines

The study determined optimal parameters to four preprocessing techniques for mid-infrared (MIR) spectra of wines and grape berry homogenates and tested MIR’s ability to model sensory properties of research Cabernet Sauvignon and Chardonnay wines. Savitsky-Golay (SG) derivative, smoothing points, and polynomial order, and extended multiplicative signal correction (EMSC) polynomial were investigated as preprocessing techniques at 2, 2, 5, and 3 levels, respectively, all in combination. Preprocessed data were analysed with partial least squares regression (PLS) to model the wine sensory data and the regression coefficients of PLS calibration models (R2) were further analysed with multivariate analysis of variance (MANOVA). SG transformations were significant factors from the MANOVA that influenced R2, while EMSC did not. Overall, PLSR models that predicted wine sensory characteristics gave a poor to moderate R2. Consistently predicting wine sensory attributes within a variety and across vintages is challenging, regardless of using grape or wine spectra as predictors.

Maximum approximate Bernstein likelihood estimation in proportional hazard model for interval-censored data.

Maximum approximate Bernstein likelihood estimates of the baseline density function and the regression coefficients in the proportional hazard regression models based on interval-censored event time data result in smooth estimates of the survival functions which enjoys an almost n1/2 -rate of convergence faster than the n1/3 -rate for the existing estimates. The proposed method was shown by a simulation to have better finite sample performance than its main competitors. Some examples including real data are used to illustrate the usage of the proposed method.

How to Bring Surgery to the Next Level: Interpretable Skills Assessment in Robotic-Assisted Surgery

Introduction: A surgeon’s technical skills are an important factor in delivering optimal patient care. Most existing methods to estimate technical skills remain subjective and resource intensive. Robotic-assisted surgery (RAS) provides a unique opportunity to develop objective metrics using key elements of intraoperative surgeon behavior which can be captured unobtrusively, such as instrument positions and button presses. Recent studies have shown that objective metrics based on these data (referred to as objective performance indicators [OPIs]) correlate to select clinical outcomes during robotic-assisted radical prostatectomy. However, the current OPIs remain difficult to interpret directly and, therefore, to use within structured feedback to improve surgical efficiencies. Methods: We analyzed kinematic and event data from da Vinci surgical systems (Intuitive Surgical, Inc., Sunnyvale, CA, USA) to calculate values that can summarize the use of robotic instruments, referred to as OPIs. These indicators were mapped to broader technical skill categories of established training protocols. A data-driven approach was then applied to further sub-select OPIs that distinguish skill for each technical skill category within each training task. This subset of OPIs was used to build a set of logistic regression classifiers that predict the probability of expertise in that skill to identify targeted improvement and practice. The final, proposed feedback using OPIs was based on the coefficients of the logistic regression model to highlight specific actions that can be taken to improve. Results: We determine that for the majority of skills, only a small subset of OPIs (2–10) are required to achieve the highest model accuracies (80–95%) for estimating technical skills within clinical-like tasks on a porcine model. The majority of the skill models have similar accuracy as models predicting overall expertise for a task (80–98%). Skill models can divide a prediction into interpretable categories for simpler, targeted feedback. Conclusion: We define and validate a methodology to create interpretable metrics for key technical skills during clinical-like tasks when performing RAS. Using this framework for evaluating technical skills, we believe that surgical trainees can better understand both what can be improved and how to improve.

Predicting the Development of Myocardial Infarction in Middle-Aged Adults with Type 2 Diabetes: A Risk Model Generated from a Nationwide Population-Based Cohort Study in Korea.

BackgroundMost of the widely used prediction models for cardiovascular disease are known to overestimate the risk of this disease in Asians. We aimed to generate a risk model for predicting myocardial infarction (MI) in middle-aged Korean subjects with type 2 diabetes.MethodsA total of 1,272,992 subjects with type 2 diabetes aged 40 to 64 who received health examinations from 2009 to 2012 were recruited from the Korean National Health Insurance database. Seventy percent of the subjects (n=891,095) were sampled to develop the risk prediction model, and the remaining 30% (n=381,897) were used for internal validation. A Cox proportional hazards regression model and Cox coefficients were used to derive a risk scoring system. Twelve risk variables were selected, and a risk nomogram was created to estimate the 5-year risk of MI.ResultsDuring 7.1 years of follow-up, 24,809 cases of MI (1.9%) were observed. Age, sex, smoking status, regular exercise, body mass index, chronic kidney disease, duration of diabetes, number of anti-diabetic medications, fasting blood glucose, systolic blood pressure, total cholesterol, and atrial fibrillation were significant risk factors for the development of MI and were incorporated into the risk model. The concordance index for MI prediction was 0.682 (95% confidence interval [CI], 0.678 to 0.686) in the development cohort and 0.669 (95% CI, 0.663 to 0.675) in the validation cohort.ConclusionA novel risk engine was generated for predicting the development of MI among middle-aged Korean adults with type 2 diabetes. This model may provide useful information for identifying high-risk patients and improving quality of care.

A multi-scale approach for testing and detecting peaks in time series

ABSTRACT An approach is presented that combines a statistical test for peak detection with the estimation of peak positions in time series. Motivated by empirical observations in neuronal recordings, we aim at investigating peaks of different heights and widths. We use a moving window approach to compare the differences of estimated slope coefficients of local regression models. We combine multiple windows and use the global maximum of all different processes as a test statistic. After rejection, a multiple filter algorithm combines peak positions estimated from multiple windows. Analysing neuronal activity recorded in anaesthetized mice, the procedure could identify significant differences between two brain states concerning peak occurrences and intermediate down states showing no peaks. This suggests that the method can be useful in the analysis of time series showing variability of peak shapes. The method is implemented in the -package (available on CRAN).

Forecasting the North Atlantic Oscillation index using altimetric sea level anomalies

The objective of this paper is to present a new approach for forecasting NAO index (NAOi) based on predictions of sea level anomalies (SLAs). We utilize significant correlations (Pearson’s r up to 0.69) between sea surface height (SSH) calculated for the North Atlantic (15–65°N, basin-wide) and winter Hurrell NAOi, as shown by Esselborn and Eden (Geophys Res Lett 28:3473–3476, 2001).We consider the seasonal and monthly data of Hurrell NAOi, ranging from 1993 to 2017. Weekly prognoses of SLA are provided by the Prognocean Plus system which uses several data-based models to predict sea level variation. Our experiment consists of three steps: (1) we calculate correlation between the first principal component (PC1) of SSH/SLA data and NAOi, (2) we determine coefficients of a linear regression model which describes the relationship between winter NAOi and PC1 of SLA data (1993–2013), (3) we build two regression models in order to predict winter NAOi (by attaching SLA forecasts and applying coefficients of the fitted regression models). The resulting 3-month prognoses of winter NAOi are found to reveal mean absolute errors of 1.5 or less. The choice of method for preparing SLA data for principal component analysis is shown to have a stronger impact on the prediction performance than the selection of SLA prediction method itself.

Measurement and zonation of soil surface moisture in arid and semi-arid regions using Landsat 8 images

Monitoring of soil surface moisture is an imperative factor in water and energy cycle. Due to the variability of soil characteristics such as topography, vegetation, and climate dynamics, this important factor varies with respect to time and place. Measuring methods can provide soil moisture information in a wide range of short intervals with reasonable accuracy. In present research, Landsat 8 satellite data with various soil moisture content estimation methods were tested. In order to evaluate the accuracy of each method, the real-field data used 80 samples of volumetric soil moisture content in suburban areas of Semnan city that were collected at the time of satellite passage of the area. Some of the indicators used in this study are normalized vegetation index, NDTI index, NDMI index, PSMI index (use full form of these indices), surface temperature, and SMSWIR index. The SMSWIR index with correlation coefficient was 0.78, and the correlation coefficient of regression model was 0.61, and RMSE was 3.69. The results of the regression model and real data were estimated to be 3.69, which are recommended for assessing surface soil moisture in arid and desert regions. Three indicators of SMSWIR index, NDTI index, and NDMI index with a small difference are not suitable indices for measuring soil moisture content in desert areas with vegetation cover. By employing multivariable regression models, soil moisture model was also prepared by using the studied indices. The findings of this research indicate that the simultaneous correlation model is superior to the surface soil moisture mapping.

Effect of E-Financial Documentation and Statements on Operating Costs in Universities in Kenya.

Institutions globally have embraced electronic systems of managing documents and generation of financial statements ranging from maintenance of ledgers, extraction of the trail balance and automated income statement and statement of financial position. This study sought to analyze the effect of e-financial documentation and statements on operating costs in universities in Kenya. The study was guided by theory of reasoned action and technology acceptance model. The target population was 80 employees drawn from 8 universities, four public universities and four private universities operating in Mount Kenya region. Census was used to collect the data from target population. A self-administered semi-structured questionnaire was distributed to the target population. A total of 71 questionnaires were returned giving a response rate of 88.75%. Statistical package was used to undertake descriptive and inferential statistical analysis. Descriptive analysis was done using both means and standard deviations. Bivariate linear regressions were used to assess the effect of e-financial documentation and statements on operating costs. Model R2, ANOVA Statistics (F Statistic and associated p-value) and regression coefficients (Beta and associated p-value) were generated and interpreted. The Bivariate results indicated that online financial documentation have a significant effect on the operating cost of universities. Electronic financial statements had an insignificant effect on operating costs of universities. This study concludes that online financial documentation affects organization costs in the universities. The study recommends the need to sensitize management of universities and other organizations on the importance of electronic financial services because they could reduce operating costs.

Intelligent evaluation of storage period of green tea based on VNIR hyperspectral imaging combined with chemometric analysis

Tea storage period is difficult to be assessed by traditional sensory and chemical analysis methods. In this research, hyperspectral imaging (HSI) covering the spectral range of 400–1000 nm was combined with chemometric methods to predict the storage period of green tea samples. Tea samples were incubated under two storage temperatures of 4 °C (Sample set 1) and 25 °C (Sample set 2) for 360 days. The reflectance spectra were acquired every 90 days. Based on the preprocessed full spectra, partial least squares-discriminant analysis (PLS-DA)- and support vector machine (SVM)-based models had acceptable outputs for the two sample sets with an accuracy of over 90%. In addition, regression coefficients (RCs) of PLS-based models, second derivative (2-Der), and successive projections algorithm (SPA) were applied to select the optimal wavelengths. Satisfactory results were achieved by SVM-based models on the basis of optimal wavelengths. The SPA-SVM- and 2-Der-SVM-based models had the best performance to discriminate the storage period of the two sample sets with the prediction accuracies of 98% and 98%, respectively. The results demonstrated that HSI could be used to evaluate the storage period of green tea, providing an alternative for a real-time monitoring of tea quality.