Standard Linear Regression Methods Research Articles

An Assessment of the Relevance of the Mincerian Equation: The Case of Mauritius

This paper assesses the relevance of the Mincerian Equation in Mauritius. Using CMPHS data, the basic equation is augmented to cater for wage responsiveness to changes across different schooling levels. However, the use of the standard linear regression method does not take into consideration the endogeneity of the variable years of schooling and sample selection bias. The IV2SLS Method is adopted, using region as an instrument. The Heckit model is further adopted to address the problem of sample selection bias. This study also employs pseudo panel models as an alternative to IV2SLS as a weak instrument may significantly impact results.

Constraints on the Fault Dip Angles of Lunar Graben and Their Significance for Lunar Thermal Evolution

Lunar grabens are the largest tensional linear structures on the Moon. In this paper, 17 grabens were selected to investigate the dips and displacement–length ratios (γ) of graben-bounding faults. Several topographic profiles were generated from selected grabens to measure their rim elevation, width and depth through SLDEM2015 (+LOLA) data. The differences in rim elevation (∆h) and width (∆W) between two topographic profiles on each graben were calculated, yielding 146 sets of data. We plotted ∆h vs. ∆W for each and calculated the dip angle (α) of graben-bounding faults. A dip of 39.9° was obtained using the standard linear regression method. In order to improve accuracy, large error data were removed based on error analysis. The results, 49.4° and 52.5°, were derived by the standard linear regression and average methods, respectively. Based on the depth and length of grabens, the γ value of the graben-bounding normal fault is also studied in this paper. The γ value is 3.6 × 10−3 for lunar normal faults according to the study of grabens and the Rupes Recta normal fault. After obtaining the values of α and γ, the increase in lunar radius indicated by the formation of grabens was estimated. We suggest that the lunar radius has increased by approximately 130 m after the formation of grabens. This study could aid in the understanding of normal fault growth and provide important constraints on the thermal evolution of the Moon.

A Better Method to Calculate Fuel Burnup in Pebble Bed Reactors Using Machine Learning

Burnup measurement is an important step in material control and accountancy at nuclear reactors and may be done by examining gamma spectra of fuel samples. Traditional approaches rely on known correlations to specific photopeaks (e.g., Cs) and operate via a standard linear regression method. However, the quality of these regression methods is limited even in the best case and is significantly poorer at short fuel cooldown times, due to the elevated radiation background by short-lifetime isotopes and self-shielding effect of the fuel. For practical operation of pebble bed reactors (PBRs), quick measurements (in minutes) and short cooling times (in hours) are required from a safety and security perspective. We investigated the efficacy and performance of machine learning (ML) methods to predict the burnup of the pebble fuel from full gamma spectra (rather than specific discrete photopeaks) and found a full-spectrum ML approach to far outperform baseline regression predictions in all measurement and cooling conditions, including in operational-like measurement conditions. We also performed model and data ablation experiments to determine the relative performance impact of our ML methods’ capacity to model data nonlinearities and the inherent additional information in full spectra. Applying our ML methods, we found a number of surprising results, including improved accuracy at shorter fuel cooling times (the opposite of the norm), remarkable robustness to spectrum compression (via rebinning), and competitive burnup predictions even when using a background signal only (i.e., explicitly omitting known isotope photopeaks).

Learning the Indicative Patterns of Simulated Force Changes in Soil Moisture by BP Neural Networks and Finding Differences with SMAP Observations

Soil moisture is a vital land surface variable that can influence climate change. Many problems in soil moisture data require the identification of signals obscured by anthropogenic external forces (including greenhouse gases such as CO2 and aerosol radiative force), natural forces (such as volcanic and solar activity), and internal variability (such as ENSO, NAO, and PDO). Although artificial neural networks (ANNs) have been widely studied in making accurate predictions, the studies of interpretation of ANNs in soil moisture are still rare. Hence, the proposed method aims to assist in the study of interpretating soil moisture data. Specifically, first, an ANN model is trained to predict the approximate year of the simulations by identifying the spatial patterns of qualitative changes in soil moisture. After accurately predicting the approximate year, the spatial patterns in the ANN model, acting as “reliable indicators” of the force changes, are the different natures of regional signals. Then, the simulated data and Soil Moisture Active and Passive (SMAP) observations are fed into the trained ANN separately, and the specific differences are observed by the Deep Taylor Decomposition (DTD) visualization tool. By comparing with the standard multiple linear regression method, the results of the ANN model can provide the reliable indicators of change for a specific year, thus providing meaningful information from the ANN model according to the common soil moisture data. The results show that a large correlation exists between eastern Asia and western North America during the 21st century, and the correlation increases with time in Australia. This also reflects the strong force signal due to a combination of anthropogenic and external forces that has played a role in soil moisture over the decades and can clearly discern the differences between model simulations and observed data. This study indicates that the proposed method using ANNs and visualization tools enables relatively accurate predictions and the discovery of unknown patterns within soil moisture data.

Thermal requirements for egg development of two endemic Wiseana pest species (Lepidoptera: Hepialidae) of economic importance

AbstractSeveral endemic hepialid species in the genus Wiseana have become serious pests in New Zealand. Two species, in particular, reach high numbers, causing significant economic damage to pasture. Key developmental parameters for egg hatch for both species are needed along with monitoring and weather data to estimate the optimal time to apply control measures to the vulnerable early instar larvae. In this study, thermal requirements for the egg stage of Wiseana copularis and W. cervinata were determined to provide the basis of a prototype phenology model designed to improve the timing of application, therefore, the efficacy of an insect growth regulator, diflubenzuron, against early instar larvae. The standard linear regression method and the Ikemoto and Takai method of insect development were compared for the goodness of fit to the egg‐stage development of W. copularis and W. cervinata at constant temperatures to determine a developmental lower threshold (T0) and degree day (DD) requirement for the egg to the larval stage for each species. The Ikemoto and Takai method gave the highest estimate of T0 for W. copularis at 7.9°C compared with 6.4°C for the standard regression method. For W. cervinata, the estimates ranged from 6.6°C (Ikemoto and Takai method) to 4.1°C (standard regression method). Of the two methods, the Ikemoto and Takai method had the better fit and lowest standard error for the parameters of interest. Thus, the Ikemoto and Takai model was chosen to estimate the critical parameters, T0 and the requirement for egg‐larval development of W. copularis (7.9°C, 185 DD) and W. cervinata (6.6°C, 177 DD).

Investigating the Effect of Emotional Intelligence on Job Performance (Case Study: Employees of Islamic Azad University, Qaemshahr Branch)

Objective: The purpose of this research was to investigate the effect of emotional intelligence on job performance. Methodology: The research method is descriptive and survey type and the statistical population is the employees of Islamic Azad University of Qaemshahr Branch. The number of members of the society was 210 people that by using Cochran's formula, 136 people were randomly selected as samples. The data gathering tool was standard questionnaire for each component of emotional intelligence and job performance style. Results: Data were analyzed using descriptive and inferential level using mean and standard deviation and linear regression methods. Conclusion: The results showed that emotional intelligence and its dimensions (self-awareness, self-regulation, self-motivation, social awareness, and social skills) have a significant effect on job performance.

Underground Sagnac gyroscope with sub-prad/s rotation rate sensitivity: Toward general relativity tests on Earth

Measuring in a single location on Earth its angular rotation rate with respect to the celestial frame, with a sensitivity enabling access to the tiny Lense-Thirring effect is an extremely challenging task. GINGERINO is a large frame ring laser gyroscope, operating free running and unattended inside the underground laboratory of the Gran Sasso, Italy. The main geodetic signals, i.e., Annual and Chandler wobbles, daily polar motion and Length of the Day, are recovered from GINGERINO data using standard linear regression methods, demonstrating a sensitivity better than 1 prad/s, therefore close to the requirements for an Earth-based Lense-Thirring test.

Links between freight trip generation rates, accessibility and socio-demographic variables in urban zones

This paper proposes an assessment of the links between freight trip generation (FTG) rates and accessibility. First, the paper overviews the background, sets the context and motivates the research. Second, it presents the proposed methodology, which combines an FTG model, two accessibility indicators and a linear regression analysis to assess the relationships between freight trip demand and a set of socio-demographic variables including accessibility. The FTG modelling framework, adapted from previous works, allows estimating the number of freight trips with a small amount of standard data, even when no surveyed data is available. The two gravity accessibility indexes, one potential and one exponential, are defined in the continuity of recent freight accessibility works. To those indicators, a set of socio-demographic variables, including population, area or a zone (or density), are introduced. The relationships between FTG and all those variables are assessed via standard linear regression methods completed by the verification of the corresponding linear relationship hypotheses. Third, the framework is applied to the urban area of Lyon (France), where no urban goods survey data is available. Results show that potential accessibility seems to have a better correlation to FTG and could be a good decision support indicator when combined with the population as an explanatory variable. The population can be added to accessibility as an explanatory variable, the resulting models with two variables have a slightly lower accuracy but remains close to that of models with only accessibility as an explanatory variable. This work remains exploratory and finishes by proposing practical implications and further development lines.

Linearization of the Disposition Index equation allows evaluation of secretion-sensitivity coupling slopes

AimsThe Disposition Index (DI) is widely used in clinical studies of β-cell function. However, direct physiologic interpretation of the DI value and the inverse exponential slope relating insulin secretion and insulin sensitivity terms is difficult. We evaluated a linearization of the relationship that allows separate evaluation of the DI term and the slope. MethodsInsulin secretion and sensitivity indices were derived from standardized oral glucose tolerance testing, including commonly used terms and model-derived terms. The population included participants with normoglycemia, dysglycemia or Type 2 diabetes. Logarithmic transformation of the DI equation to linearize the secretion-sensitivity relationship was performed, and the resulting secretion-sensitivity relationships were evaluated using standard linear regression methods. ResultsSimple logarithmic transformation linearized the secretion-sensitivity relationships available from a variety of OGTT-derived metrics. In normoglycemic subjects the slopes approximated −1 in insulin-basedsecretion-sensitivity pairs, and approximated −0.6 in C-peptide based secretion-sensitivity pairs. Group differences in DI terms were observed as expected. These analyses also revealed differing secretion-sensitivity slopes, with IGT and T2D demonstrating progressively impaired coupling. ConclusionsLinearization of the secretion-sensitivity relationship provides simplified interpretation of the DI value and allows simple analysis and meaningful interpretation of the secretion-sensitivity slope. This linear relationship is amenable to standard statistical evaluations for comparisons of insulin secretion responses and of secretion-sensitivity coupling across groups.

Selected Characteristics of Atmospheric Precipitation on the Polish Coast of the Baltic Sea

The characteristics of extreme precipitation, including dominant trends, were analyzed for eight stations located on the Polish coast of the Baltic Sea for the period 1951-2010. Maximum 1-day and 5-day precipitation totals, events exceeding the 90th and 95th percentile calculated for the period 1961-1990 applied as indices enabling the assessment of the frequency of wet conditions. Annual trends were calculated using the standard linear regression method, while the fit of the model was assessed with the t-test at the 95% confidence level. Annual precipitation reaches values from 775 mm on the east coast to 683 mm on the central coast and shows positive trends on six out of eight analyzed stations, from 0.8 mm·year-1 in Swibno to 2.5 mm·year−1 in Dziwnow. The maximum 1-day precipitation totals range between 58.7 mm on the western coast to 105.0 mm on the central coast and show statistically significant trends (0.1 - 0.2 mm·year−1) in this area. There are, on average, six to seven 5-day rainfall periods recorded annually in the investigated area. The annual number of 5-day precipitation periods shows positive statistically significant trends in the central and eastern part of the South Baltic coast (0.2-0.3 per 10 years). The median of the maximum 5-day precipitation totals ranges from 21.6 mm in Swinoujscie to 78.6 mm in Ustka. Trends in changes in maximum 5-day rainfall totals reach the level of 0.1-0.2 mm·year−1 and are statistically significant in stations from Swinoujscie to Ustka and in Gdynia. The values of the 90th percentile of 1-day precipitation total are equal to 8-10 mm. Annually, there are about 16-18 days with such precipitation recorded in the area under investigation. The trends are positive (0.1-1.0 day per 10 years) on the central coast. The values of the 95th percentile of 1-day precipitation totals reach 11.5-14.0 mm. Annually, there are about 8-9 days with such precipitation recorded in the area under investigation. The trends are positive (0.3-0.4 days·10 years−1) on the western and central coast.

Trends in Selected Characteristics of Precipitation in the Cities above 100,000 Residents in Poland, 1966-2016

Contemporary climate change causes an increase in the sensitivity of urban areas to the effects of this change, manifested in the increased threat of periods of both shortage and surplus of water or a higher probability of flood or drought occurrence. That is why Polish Ministry of the Environment is currently implementing project aimed at developing in major Polish cities with more than 100,000 inhabitants Urban Adaptation Plans that would reduce the city’s vulnerability to climate change. One of the elements of preparing such plans is the assessment of the city’s exposure to the effects of changes in rainfall conditions. Therefore, it is extremely important to know the trends of these changes. The analysis of the variability of selected precipitation characteristics was carried out for 24 Polish cities with more than 100,000 inhabitants. The tendencies of annual and seasonal precipitation changes, the number of days with precipitation, the number of days with extreme precipitation (events exceeding the 19712000 95th percentile of daily precipitation) as well as the fraction of annual total precipitation due to extreme precipitation have been characterized. Annual trends were calculated using standard linear regression method and its statistical significance at the 95% confidence level was assessed with the help of F Snedecor probability distribution. Changes in precipitation characteristics show mixed patterns. In the majority of analyzed cities, annual precipitation totals tend to decrease, however, there is no clear spatial pattern. In general, seasonal tendencies of summer and winter precipitation, and especially autumn precipitation, also show negative tendencies. However, the sums of precipitation in the spring season are generally characterized by positive tendencies. In the vast majority of analyzed cities, the number of days with precipitation decreases, as well as the number of days with extreme precipitation. Decreasing or stable precipitation together with increasing air temperature may result in water insecurity in many Polish cities.

Extreme Precipitation in Poland in the Years 1951-2010

The characteristics of extreme precipitation, including the dominant trends, were analysed for eight stations located in different parts of Poland for the period 1951-2010. Five indices enabling the assessment of the intensity and frequency of both extremely dry and wet conditions were applied. The indices included the number of days with precipitation ≥10mm·d-1 (R10), maximum number of consecutive dry days (CDD), maximum 5-day precipitation total (R5d), simple daily intensity index (SDII), and the fraction of annual total precipitation due to events exceeding the 95th percentile calculated for the period 1961-1990. Annual trends were calculated using standard linear regression method, while the fit of the model was assessed with the F-test at the 95% confidence level. The analysed changes in extreme precipitation showed mixed patterns. A significant positive trend in the number of days with precipitation ≥10mm·d-1 (R10) was observed in central Poland, while a significant negative one, in south-eastern Poland. Based on the analysis of maximum 5-day precipitation totals (R5d), statistically significant positive trends in north-western, western and eastern parts of the country were detected, while the negative trends were found in the central and northeastern parts. Daily precipitation, expressed as single daily intensity index (SDII), increased over time in northern and central Poland. In southern Poland, the variation of SDII index showed non-significant negative tendencies. Finally, the fraction of annual total precipitation due to the events exceeding the 1961-1990 95th percentile increased at one station only, namely, in Warsaw. The indicator which refers to dry conditions, i.e. maximum number of consecutive dry days (CDD) displayed negative trends throughout the surveyed area, with the exception of Szczecin that is a representative of north-western Poland.

A Simple Nonlinear and End-Member-Free Approach for Obtaining Ocean Remineralization Patterns

AbstractThe variability of a biogeochemical property in the ocean is the outcome of both nonconservative (such as respiration and photosynthesis) and conservative (mixing of water masses with distinct concentrations at origin) processes. One method to separate both contributions is based on a multiple regression of the biogeochemical property in terms of temperature θ and salinity S as conservative proxies of water masses. This regression delivers the variability related to the conservative fraction and hence allows for identifying the residual as the biogeochemical anomaly. Here, the standard multiple linear regression (MLR) method, which assumes that water masses mix locally and linearly, is compared with a nonlinear polynomial regression (PR) over the entire (θ, S) space. The PR method has two important advantages over MLR: allows for simultaneous nonlinear mixing of all water masses and does not require knowing the end-member water types. Both approaches are applied to data along 7.5°N in the equatorial Atlantic Ocean, and the biogeochemical anomalies are calculated for humic-like fluorescent dissolved organic matter, apparent oxygen utilization, and nitrate—all of them related through in situ remineralization processes. The goodness of both approaches is assessed by analyzing the linear dependence and the coefficient of correlation between the anomalies. The results show that the PR method can be applied over the entire water column and yet retains the local variability associated with nonconservative processes. The potential of the PR approach is also illustrated by calculating the oxygen–nitrate stoichiometric ratio for the entire 7.5°N transatlantic section.

Bayesian estimation of directed functional coupling from brain recordings.

In many fields of science, there is the need of assessing the causal influences among time series. Especially in neuroscience, understanding the causal interactions between brain regions is of primary importance. A family of measures have been developed from the parametric implementation of the Granger criteria of causality based on the linear autoregressive modelling of the signals. We propose a new Bayesian method for linear model identification with a structured prior (GMEP) aiming to apply it as linear regression method in the context of the parametric Granger causal inference. GMEP assumes a Gaussian scale mixture distribution for the group sparsity prior and it enables flexible definition of the coefficient groups. Approximate posterior inference is achieved using Expectation Propagation for both the linear coefficients and the hyperparameters. GMEP is investigated both on simulated data and on empirical fMRI data in which we show how adding information on the sparsity structure of the coefficients positively improves the inference process. In the same simulation framework, GMEP is compared with others standard linear regression methods. Moreover, the causal inferences derived from GMEP estimates and from a standard Granger method are compared across simulated datasets of different dimensionality, density connection and level of noise. GMEP allows a better model identification and consequent causal inference when prior knowledge on the sparsity structure are integrated in the structured prior.

Associations of stressful life events with coping strategies of 12-15-year-old Norwegian adolescents.

Successful adaptation to the environment requires strategies to cope with stressful situations. The aim of this study was to examine the role of stressful life events in coping strategies during early adolescence. A representative sample of 2464 adolescents in Norway were assessed at two time-points, one year apart (i.e., at T1, mean age 13.7years, and at T2, mean age 14.9years), with identical questionnaires. The participation rate was 88.3% at T1. Stressful life events and daily hassles were measured by questionnaires constructed for this study. Coping with stress was measured by a modified version of the Coping Inventory for Stressful Situations (CISS), which measures three coping dimensions: emotional, task and avoidance coping. Depressive symptoms were assessed by the Mood and Feelings Questionnaire (MFQ). Standard multiple linear regression methods were applied. Different domains of stressful life events were associated with the coping strategies, and these relationships differed at various time-points by gender. In sum, school stress and stressful life events in one's network (network stress) was associated with coping strategies more strongly among girls, while family and miscellaneous stress showed a stronger association among boys. These relationships were partly mediated by depressive symptom levels, more strongly in cross-sectional than in longitudinal analyses. However, daily hassles seemed to represent smaller events of no importance in coping strategies. In preventive work, reducing stressful events, treating depression and teaching healthier coping strategies are important.

YES as a Tool for Detecting Estrogenic Activity of Some Food Additives Compounds: E 104, E 122, E 124, E 132 and E 171

The toxicity (mutagenic and carcinogenic effects) of food dyes (used like food additive) has been well documented. However the endocrine disrupting effects of these dyes have been poorly investigated. We studied five commercial food dyes for their agonistic estrogen activity and do a comparison with estrogenic activity of some paraben (other food additives compounds). Total estrogenic activities were measured using the Yeast Estrogen Screen bioassay (YES). The estrogenic potency of the food dyes was measured by dose-response curves and compared to a doseresponse curve of 17-β-estradiol (E2), (reference compound). Estrogen EC50 values have been calculated using standard linear regression methods. Among these compounds, three food dyes, indigotine (E 132), the monoazo dye Ponceau 4R (E 124) and the inorganic dye titanium dioxide (E 171), revealed weak estrogenic activity. Negative results were obtained for Quinoline Yellow (E 104).

Coping Strategies and Associations With Depression Among 12- to 15-Year-Old Norwegian Adolescents Involved in Bullying

The aim of this study was to examine coping styles among young adolescents involved in bullying, both as victims or aggressors, and the relationships between coping styles and depressive symptom levels. The possible moderating and mediating roles of coping in the relationships between bullying involvement and depression are also investigated. A representative community sample of 2464 adolescents was assessed. Coping styles were measured by the Coping Inventory for Stressful Situations. Depressive symptoms were assessed by the Mood and Feelings Questionnaire. Analysis of variance and standard linear regression methods were applied. Adolescents being bullied or being aggressive toward others both showed more emotional coping than did noninvolved adolescents (p < 0.001). Being bullied and high emotional, low task, and high avoidant coping styles were independently related to more depressive symptoms. The association between being bullied and depressive symptoms was both moderated and partially mediated by emotional coping.

The Meta Taylor Rule

We characterise U.S. monetary policy within a generalized Taylor rule framework that accommodates uncertainties about the duration of policy regimes and the specification of the rule, in addition to the standard parameter and stochastic uncertainties inherent in traditional Taylor rule analysis. Our approach involves estimation and inference based on Taylor rules obtained through standard linear regression methods, but combined using Bayesian model averaging techniques. Employing data that were available in real time, the estimated version of the 'meta' Taylor rule provides a flexible but compelling characterisation of monetary policy in the United States over the last forty years.

Competing process hazard function models for player ratings in ice hockey

Evaluating the overall ability of players in the National Hockey League (NHL) is a difficult task. Existing methods such as the famous "plus/minus" statistic have many shortcomings. Standard linear regression methods work well when player substitutions are relatively uncommon and scoring events are relatively common, such as in basketball, but as neither of these conditions exists for hockey, we use an approach that embraces the unique characteristics of the sport. We model the scoring rate for each team as its own semi-Markov process, with hazard functions for each process that depend on the players on the ice. This method yields offensive and defensive player ability ratings which take into account quality of teammates and opponents, the game situation, and other desired factors, that themselves have a meaningful interpretation in terms of game outcomes. Additionally, since the number of parameters in this model can be quite large, we make use of two different shrinkage methods depending on the question of interest: full Bayesian hierarchical models that partially pool parameters according to player position, and penalized maximum likelihood estimation to select a smaller number of parameters that stand out as being substantially different from average. We apply the model to all five-on-five (full-strength) situations for games in five NHL seasons.

Early predictors of hematoma resorption rate in medically treated patients with spontaneous supratentorial hemorrhage

Spontaneous intracranial hemorrhages are associated with a relatively high mortality rate, and there is no effective treatment so far. Hematoma resorption speed after intracranial hemorrhage (ICH) is believed to correlate with clinical outcome. However, little is known about hematoma resorption rates following spontaneous ICH. The aim of this study is to identify factors that can influence the rate of hematoma resorption in patients with spontaneous supratentorial ICH. We studied 80 patients admitted at the First Affiliated Hospital of Xi'an JiaoTong University from November 2008 to April 2012. The rate of hematoma resorption was calculated for each patient by measuring the variation in the volume of the hematoma (mL) from two computerized tomography brain scans divided by the time factor (days) separating the respective scans. Non-parametric and standard multiple linear regression methods were used for statistical analysis. The size of the hematoma was identified as a predictor of the rate of hematoma resorption. For supratentorial hematomas with a maximum volume of 45mL, the larger the volume, the greater the rate of resorption. Non-hypertensive patients had a more favorable rate of hematoma resorption than those who were hypertensive. A low serum high-density lipoprotein (HDL) level (<0.83mmol/L) was associated with a slower hematoma resorption rate. Therefore, a spontaneous ICH hematoma of less than 45mL, a history of chronic hypertension, and a lower level of HDL were found to be the predictors of the hematoma resorption rate in the first 7-day period following ICH onset.