Ordinary Regression Method Research Articles

Response of Trichogramma brassicae (Hym.: Trichogrammatidae) to temperature: Utilizing thermodynamic models to describe curvilinear development

Temperature has profound effects on biological efficiency of parasitoid wasps. In the present study, effects of seven constant temperatures (12, 15, 20, 25, 30, 32 and 35 ± 0.5 °C) on the pre-adult development of Trichogramma brassicae (Hymenoptera: Trichogrammatidae) were investigated on Sitotroga cerealella (Lepidoptera: Gelechiidae). Temperature effects on the emergence rate and sex ratio were also determined. The development rate of T. brassicae was described using two linear and twenty non-linear functions and critical bioclimatic temperatures (i.e., Tmin, Topt, Tmax and k) were estimated. Efficiency of models was assessed by different criteria (i.e., R2, R2adj, SSE, AIC, AICc, BIC and zi). Rate of development increased from 12 to 30 °C but decreased at two higher temperatures (i.e., 32 and 35 °C). At all thermal regimes a higher number of female individuals was emerged than males (1.81, 2.23, 2.03, 1.44, 1.78 and 1.78 ♀: ♂, respectively). At 12 °C, the lowest rate of parasitoid emergence (9%) was observed. The lower threshold predicted by the two linear models were 8.13 (Ordinary regression method) and 8.36 °C (Ikemoto method). These models predicted the thermal constant for pre-adult development as 181 and 177 DD, respectively. The estimates of optimum temperature ranged from 30 to 32 °C with an upper threshold between 36 and 41 °C, depending on which model was used. Based on both biological and statistical goodness-of-fit criteria, the non-linear model of Lactin-1 had the best described the experimental data. The information herein highlighted the higher performance of T. brassicae at tropical conditions and can be helpful to predict possible effects of temperature on developmental rate, select the best period of time for inundative release and improve mass rearing protocols of this parasitoid wasp.

Locally Weighted Ridge Regression for Power System Online Sensitivity Identification Considering Data Collinearity

Power system operations data are sometimes limited in a given space due to system collinearity. As such, the operations data recorded around an operating point of concern may be deficient or isotropically dispersed. Consequently, online sensitivity identification using ordinary regression methods is prone to large errors. In this paper, a locally weighted ridge regression method is proposed to overcome this problem. The norm-2 Tikhonov–Phillips regularization is integrated into the locally weighted linear regression. The integrated algorithm then has the ability to keep the online sensitivity identification stable if data are collinear while also accommodating the nonlinear and time-varying properties of the sensitivities. The mathematical derivation, online tuning, implementation, and practical considerations of the proposed method are presented. Its effectiveness is validated in a simulation system with operations data measured from real power systems.

Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables.

To obtain predictors of suicidal ideation, which can also be used for an indirect assessment of suicidal ideation (SI). To create a classifier for SI based on variables of the Patient Health Questionnaire (PHQ) and sociodemographic variables, and to obtain an upper bound on the best possible performance of a predictor based on those variables. From a consecutive sample of 9025 primary care patients, 6805 eligible patients (60% female; mean age = 51.5 years) participated. Advanced methods of machine learning were used to derive the prediction equation. Various classifiers were applied and the area under the curve (AUC) was computed as a performance measure. Classifiers based on methods of machine learning outperformed ordinary regression methods and achieved AUCs around 0.87. The key variables in the prediction equation comprised four items - namely feelings of depression/hopelessness, low self-esteem, worrying, and severe sleep disturbances. The generalized anxiety disorder scale (GAD-7) and the somatic symptom subscale (PHQ-15) did not enhance prediction substantially. In predicting suicidal ideation researchers should refrain from using ordinary regression tools. The relevant information is primarily captured by the depression subscale and should be incorporated in a nonlinear model. For clinical practice, a classification tree using only four items of the whole PHQ may be advocated.

A fuzzy ordinary regression method for modeling customer preference in tea maker design

Faced with fierce competition in marketplaces, manufacturers need to determine the appropriate settings of engineering characteristics of the new products so that the best customer preferences of the products can be obtained. To achieve this, functional models relating customer preferences to engineering characteristics need to be developed. As information regarding functional relationships between customer preferences are generally subjective or heuristic in nature, development of the customer preference models involve two uncertainties, namely fuzziness and randomness. Existing approaches use only fuzzy-based technologies to address the uncertainty caused by fuzziness. They are not designed to address the randomness of the observed data which is caused by a limited knowledge of the variability of influences between customer preferences and engineering characteristics. In this article, a fuzzy ordinary regression method is proposed to develop the customer preference models which are capable of addressing the two uncertainties of crispness and fuzziness of the customer preferences. A case study of a tea maker design which involves both uncertainties is used to demonstrate the effectiveness of the proposed method.

What Regional Scientists Need to Know About Spatial Econometrics

Regional scientists frequently work with regression relationships involving sample data that is spatial in nature. For example, hedonic house-price regressions relate selling prices of houses located at points in space to characteristics of the homes as well as neighborhood characteristics. Migration, commodity, and transportation flow models relate the size flows between origin and destination regions to the distance between origin and destination as well as characteristics of both origin and destination regions. Regional growth regressions relate growth rates of a region to past period own- and nearby-region resource inputs used in production.Spatial data typically violates the assumption that each observation is independent of other observations made by ordinary regression methods. This has econometric implications for the quality of estimates and inferences drawn from non-spatial regression models. Alternative methods for producing point estimates and drawing inferences for relationships involving spatial data samples is the broad topic covered by spatial econometrics. Like any sub-discipline, spatial econometrics has its quirks, many of which reflect influential past literature that has gained attention in both theoretical and applied work.This article asks the question -- what should regional scientists who wish to use regression relationships involving spatial data in an effort to shed light on questions of interest in regional science know about spatial econometric methods?

What Regional Scientists Need to Know about Spatial Econometrics

Regional scientists frequently work with regression relationships involving sample data that is spatial in nature. For example, hedonic house-price regressions relate selling prices of houses located at points in space to characteristics of the homes as well as neighborhood characteristics. Migration, commodity, and transportation flow models relate the size of flows between origin and destination regions to the distance between origin and destination as well as characteristics of both origin and destination regions. Regional growth regressions relate growth rates of a region to past period own- and nearby-region resource inputs used in production. Spatial data typically violates the assumption that each observation is independent of other observations made by ordinary regression methods. This has econometric implications for the quality of estimates and inferences drawn from nonspatial regression models. Alternative methods for producing point estimates and drawing inferences for relationships involving spatial data samples comprise the broad topic covered by spatial econometrics. Like any subdiscipline, spatial econometrics has its quirks, many of which reflect influential past literature that has gained attention in both theoretical and applied work. This article asks the question: “What should regional scientists who wish to use regression relationships involving spatial data in an effort to shed light on questions of interest in regional science know about spatial econometric methods?”

A class of regression type estimators in survey sampling

A class of linear regression models has been proposed for the estimation of population mean and total when information regarding auxiliary variate is available in survey sampling using regression method of estimation by introducing a new auxiliary variable z, which may also be a function of the auxiliary variable x. The proposed model leads to reduction in mean squared error as compared to ordinary regression method of estimation. The improvement has been demonstrated over ordinary regression estimator and also on ratio estimator with the help of an empirical example.

Design of Mass Flowrate Estimators for n-product Plants

Abstract Since product mass flowrates in metallurgical plants are usually unmeasured, they must be estimated from the stream component concentrations. This is a necessary step to initialize algorithms for data reconciliation through mass conservation constraints. It is sometimes critical as it conditions the non-linear programming algorithms that solve reconciliation problems. The weighted node imbalance estimation method is compared to the traditional n-product formula and ordinary regression methods, and optimized by selecting components that minimize flowrate estimate variances, and by removing abnormal data that could distort initial estimates.

Robustness properties of a robust partial least squares regression method

The presence of multicollinearity in regression data is no exception in real life examples. Instead of applying ordinary regression methods, biased regression techniques such as principal component regression and ridge regression have been developed to cope with such datasets. In this paper, we consider partial least squares (PLS) regression by means of the SIMPLS algorithm. Because the SIMPLS algorithm is based on the empirical variance–covariance matrix of the data and on least squares regression, outliers have a damaging effect on the estimates. To reduce this pernicious effect of outliers, we propose to replace the empirical variance–covariance matrix in SIMPLS by a robust covariance estimator. We derive the influence function of the resulting PLS weight vectors and the regression estimates, and conclude that they will be bounded if the robust covariance estimator has a bounded influence function. Also the breakdown value is inherited from the robust estimator. We illustrate the results using the MCD estimator and the reweighted MCD estimator (RMCD) for low-dimensional datasets. Also some empirical properties are provided for a high-dimensional dataset.

Modelling and parameter estimation of the enzymatic synthesis of oligosaccharides by beta-galactosidase from bacillus circulans

The aim of this research is to develop a model to describe oligosaccharide synthesis and simultaneously lactose hydrolysis. Model A (engineering approach) and model B (biochemical approach) were used to describe the data obtained in batch experiments with beta-galactosidase from Bacillus circulans at various initial lactose concentrations (from 0.19 to 0.59 mol.kg(-1)). A procedure was developed to fit the model parameters and to select the most suitable model. The procedure can also be used for other kinetically controlled reactions. Each experiment was considered as an independent estimation of the model parameters, and consequently, model parameters were fitted to each experiment separately. Estimation of the parameters per experiment preserved the time dependence of the measurements and yielded independent sets of parameters. The next step was to study by ordinary regression methods whether parameters were constant under the altering conditions examined. Throughout all experiments, the parameters of model B did not show a trend upon the initial lactose concentration when inhibition was included. Therefore model B, a galactosyl-enzyme complex-based model, was chosen to describe the oligosaccharide synthesis, and one parameter set was determined for various initial lactose concentrations. Copyright 1999 John Wiley & Sons, Inc.

Modelling and parameter estimation of the enzymatic synthesis of oligosaccharides by β‐galactosidase from Bacillus circulans

The aim of this research is to develop a model to describe oligosaccharide synthesis and simultaneously lactose hydrolysis. Model A (engineering approach) and model B (biochemical approach) were used to describe the data obtained in batch experiments with β-galactosidase from Bacillus circulans at various initial lactose concentrations (from 0.19 to 0.59 mol·kg−1). A procedure was developed to fit the model parameters and to select the most suitable model. The procedure can also be used for other kinetically controlled reactions. Each experiment was considered as an independent estimation of the model parameters, and consequently, model parameters were fitted to each experiment separately. Estimation of the parameters per experiment preserved the time dependence of the measurements and yielded independent sets of parameters. The next step was to study by ordinary regression methods whether parameters were constant under the altering conditions examined. Throughout all experiments, the parameters of model B did not show a trend upon the initial lactose concentration when inhibition was included. Therefore model B, a galactosyl-enzyme complex-based model, was chosen to describe the oligosaccharide synthesis, and one parameter set was determined for various initial lactose concentrations. © 1999 John Wiley & Sons, Inc. Biotechnol Bioeng 64: 558–567, 1999.

Further investigation on the regression method of mapping quantitative trait loci

The simple regression method of mapping quantitative trait loci (QTL) is further investigated in comparison with the mixture model maximum likelihood method under high heritabilities, dominant and missing markers. No significant difference between the two methods is detected in terms of errors of parameter estimation and statistical powers, with the exception that the estimation of residual variance provided by the regression method is confounded with part of the QTL variance. The test statistic profiles show some difference between the two methods, but the difference is only detectable at the micro level. An alternative method, referred to as iteratively reweighted least squares, is proposed, which can correct the deficiency of parameter confounding in the regression method yet retains the properties of simplicity and rapidity of the ordinary regression method. Like the existing regression method, the weighted least squares method can be useful in QTL mapping in conjunction with the permutation tests and construction of confidence intervals by bootstrapping.

Estimation of Missing Daily Temperatures: Can a Weather Categorization Improve Its Accuracy?

A method of estimating missing daily temperatures is proposed. The procedure is based on a weather classification consisting of two steps: principal component analysis and cluster analysis. At each time of observation (0700, 1400, and 2100 local time) the weather is characterized by temperature, relative humidity, wind speed, and cloudiness. The coefficients of regression equations, enabling the missing temperatures to be determined from the known temperatures at nearby stations, are computed within each weather class. The influence of various parameters (input variables, number of weather classes, number of principal components, their rotation, type of regression equation) on the accuracy of estimated temperatures is discussed. The method yields better results than ordinary regression methods that do not utilize a weather classification. An examination of statistical properties of the estimated temperatures confirms the applicability of the completed temperature series in climate studies.

Efficiency measurement by data envelopment analysis with econometric applications

A method of estimating the production frontier known as data envelopment analysis (DEA) is applied here through a set of tests to examine its robustness. The empirical application shows that in suitable cases the DEA method proves superior to the ordinary regression method as regards robustness and other measures of sensitivity of parameter estimates.

Methods of Estimating Mental Factors

MR. M. S. BARTLETT has proposed1 formulae for the estimation of mental factors as alternatives to formulae I have given2. My formulae were arrived at by the ordinary regression method. Bartlett’s estimates and the regression estimates attain different ends, and it is agreed that each method is correct in the right place. The regression estimates minimize the squares of the discrepancies between the estimates and the true values, summed over the population of persons. Bartlett's estimates minimize the squares of a man's specific factors, summed over the tests. If the scores expressed in matrix notation as linear functions of the true factors are the two sets of estimates, for the common factors only, are. The regression method has the quality that if factors estimated by it are inserted in the specification equation which defines a vocation in terms of factors, the result is the same as a direct regression estimate of vocational ability from the tests.