Applications of linear and nonlinear models: Fixed effects, random effects, and total least squares

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in education. However, when the focus of a study is on the regression coefficients at level one rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods may be advantageous because they rely on weaker assumptions than what is required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models with crossed random effects, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated. We found that CCREM performed the best when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. FE-CRVE showed the best performance when the exogeneity assumption is violated. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt.

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Impact of Full IFRS, Accounting Standards for SMES and Company Demographics on Firms’ Return on Asset and Return on Equity using Panel Data Regression

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1120.0886s19 ◽

2019 ◽

Vol 8 (6S) ◽

pp. 589-601 ◽

Cited By ~ 1

Keyword(s):

Least Squares ◽

Random Effects ◽

Fixed Effects ◽

Random Effect ◽

Ordinary Least Squares ◽

Lagrangian Multiplier ◽

Random Effects Model ◽

Fixed Effects Model ◽

Real Property ◽

The Impact

Due to globalization, markets are becoming more interconnected as the companies are engaged in doing cross-border offerings. Currently, competitions are intensified because Domestic organizations discover themselves competing with each nearby opposite numbers and worldwide companies. But one component that hinders SMEs is the need for reliable and similar monetary data. According to Abarca (2014), adoption of a high-quality and consistent set of accounting requirements is critical so as for the businesses to remain competitive in ASEAN member states. This paper ambitions to answer the query, what modified into the extent of the impact of compliance with full IFRS and IFRS for SMEs on profitability of agencies belong to real property enterprise? This paper moreover sought to decide whether there may be a sizeable distinction among the groups’ compliance with the overall PFRS and the PFRS for SMEs and to determine whether or now not there is a massive distinction among the companies’ financial normal overall performance earlier than and after the adoption of the PFRS for SMEs.Paired T-test have become employed in case you need to determine whether there is a big distinction between the agencies’ compliance with the entire PFRS and the PFRS for SMEs and to decide whether or not there may be a big difference some of the groups’ monetary performance earlier than and after the adoption of the PFRS for SMEs. Using STATA, the great appropriate version for every economic ratio on the subject of degree of compliance emerge as determined on. First, take a look at parm command became used to find out which most of the Least Squares Dummy Variable Regression Modes (LSDV1, LSDV2, LSDV3) underneath the Fixed Effects Model is the ideal version. Afterwards, Hausman Fixed Random Test changed into used to pick out out which is more suitable amongst Fixed Effects Model and Random Effects Model. If Fixed Effects Model modified into the more appropriate one, the Wald’s test turn out to be used to determine the best version among Fixed Effects Model and Ordinary Least Squares Model. On the alternative hand, if Random Effects Model became the more suitable one, the Breusch and Pagan Lagrangian Multiplier Test for Random Effect have become used to decide the satisfactory version amongst Random Effects Model and Ordinary Least Squares. Moreover, if Ordinary Least Squares became the splendid model, it is going to be in addition tested to check for heteroscedasticity and multicollinearity. White’s test became used to check for heterescedasticity and Variance Inflation Factor have become used to test if multicollinearity is gift. The results display that the adoption of PFRS for SMEs stepped forward the compliance of Philippine real property SMEs. However, no vast alternate became said inside the financial average performance of those companies (as measured with the resource of cross back on assets and go back on equity). This was further supported by the results of the panel regression. This means that despite having a relatively

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Pairwise comparisons for parallel profile models with mixed effects

Tatra Mountains Mathematical Publications ◽

10.2478/v10127-012-0007-y ◽

2012 ◽

Vol 51 (1) ◽

pp. 67-73

Author(s):

Hiroto Hyakutake

Keyword(s):

Confidence Intervals ◽

Random Effects ◽

Nonlinear Models ◽

Mixed Effects ◽

Repeated Measurements ◽

Pairwise Comparisons ◽

Simultaneous Confidence Intervals ◽

Regression Parameters ◽

The Mean ◽

Linear And Nonlinear

ABSTRACT There are several linear and nonlinear models for analyzing repeated measurements. The mean response for an individual depends on the regression parameters specific to that individual. One of the simple forms is the sum of vectors of fixed parameters and random effects. When the models with mixed effects for several groups are parallel, pairwise comparisons of level differences are considered. For the comparisons, approximate simultaneous confidence intervals are given.

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A Simulation and Experimental Study on Identification of an Electromechanical System

International Journal of Systems Applications, Engineering & Development ◽

10.46300/91015.2022.16.5 ◽

2022 ◽

Vol 16 ◽

pp. 26-33

Author(s):

Tolgay Kara ◽

Sawsan Abokoos

Keyword(s):

Experimental Data ◽

Nonlinear System ◽

Least Squares ◽

Position Control ◽

Nonlinear Models ◽

Dead Zone ◽

Nonlinear Effects ◽

Recursive Least Squares ◽

Suitable Model ◽

Linear And Nonlinear

The current applications in electromechanical energy conversion demand highly accurate speed and position control. For this purpose, a better understanding of the motion characteristics and dynamic behavior of electromechanical systems including nonlinear effects is needed. In this paper, a suitable model of Permanent Magnet Direct Current (PMDC) motor rotating in two directions is developed for identification purposes. Model is parameterized and identified via simulation and using real experimental data. Linear and nonlinear models for the system are built for identification, and the effective nonlinearities in the system, which are Coulomb friction and dead zone, are integrated into the nonlinear model. A Weiner- Hammerstein nonlinear system description is used for identification of the model. MATLAB is selected as the investigating tool, and a simulation model is used to observe the error between the simulated and estimated outputs. Identification of the linear and nonlinear system models using experimental data is performed using the least squares (LS) and recursive least squares (RLS) methods. Performance of the model and identification method with the real time experiments are presented numerically and graphically, revealing the advantages of the proposed nonlinear identification approach.

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EFEITO DO MEIO E DA IDADE DA VACA SOBRE O GANHO DE PESO NA FASE DE CRIA, RECRIA E OS DIAS PARA SE OBTER 160 KG EM ANIMAIS DA RAÇA GUZERÁ

Archives of Veterinary Science ◽

10.5380/avs.v9i1.4052 ◽

2004 ◽

Vol 9 (1) ◽

Author(s):

E. GEMIN ◽

J.C. SOUZA ◽

L.O.C. SILVA ◽

C.H.M. MALHADO ◽

P.B. FERRAZ FILHO

Keyword(s):

Environmental Factors ◽

Statistical Model ◽

Least Squares ◽

Random Effects ◽

Environmental Effects ◽

Fixed Effects ◽

Least Squares Method ◽

Data Set ◽

Quadratic Effect ◽

Daily Gain

O objetivo deste trabalho foi avaliar a influência dos efeitos de meio e da idade da vaca sobre os ganhos de peso do nascimento ao desmame (GPD), período pós-desmame (GPS) e sobre o número de dias para se obter 160 kg (D160). O rebanho avaliado continha 1.747 animais, sendo os dados analisados pelo método dos quadrados mínimos utilizando-se um modelo estatístico contendo os efeitos fixos de mês, ano e sexo do bezerro, o efeito aleatório de touros na fazenda, e como covariável a idade da vaca ao parto. As médias ajustadas para ganho de peso pré e pós-desmame, e para dias para a obtenção 160 kg foram 0,604 ± 0,01 kg; 0,399 ± 0,01 kg; em 285 ± 5,3 dias, respectivamente. Os machos foram superiores às fêmeas relativo ao GPD = 6,0%; D160 = 5,8 %, GPS = 20,1%. Quanto ao mês, as maiores médias de ganho de peso no pré-desmame recaiu nos animais nascidos no mês de agosto. Com relação aos dias para se obter 160 kg, os melhores resultados foram dos animais nascidos nos meses julho a setembro. A idade da vaca influenciou as caracteristicas ganho de peso pré-desmame e no D160. Environmental effects and age of dam on pre- and post-weaning daily gain, and on number of days to gain 160 kg from birth to weaning on guzerath breed cattle Abstract The objective of this study was to evaluate the effects of environmental factors and age of dam on pre- (GPD) and post-weaning (GPS) daily gain, and on the were number of days to gain 160 kg (D160) from birth to weaning. The data set contained 1,747 animals, and were analyzed by the least squares method. The statistical model included the fixed effects of month and year of birth, and sex of the calf and age of the dam at calving. Sire nested within farm and the error were random effects. The pre- and post-weaning average daily gains, and days to gain 160 kg least squares means were 0.604 ± 0.01 kg, 0.399 ± 0.01 kg, and 285.0 ± 5.3 days, respectively. The males were 6.0, 21.1 and 5.8% superior to the females for GPD, GPS and D160, respectively. The highest pre-weaning gain was for the animals born August. Regarding D160, the best results were for the animals born from July to September. Age of the cow showed a significant quadratic effect on the traits. The best cows were the 94-month-old ones. First calving cows produced the lightest calves. The results showed the importance of the environmental effects on the traits studied, evidencing the need for them to be corrected.

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Total Least-Squares Collocation: An Optimal Estimation Technique for the EIV-Model with Prior Information

Mathematics ◽

10.3390/math8060971 ◽

2020 ◽

Vol 8 (6) ◽

pp. 971

Author(s):

Burkhard Schaffrin

Keyword(s):

Least Squares ◽

Random Effects ◽

Prior Information ◽

Optimal Estimation ◽

Total Least Squares ◽

Data Matrix ◽

Estimation Technique ◽

Least Squares Collocation ◽

Wide Range ◽

Least Squares Adjustment

In regression analysis, oftentimes a linear (or linearized) Gauss-Markov Model (GMM) is used to describe the relationship between certain unknown parameters and measurements taken to learn about them. As soon as there are more than enough data collected to determine a unique solution for the parameters, an estimation technique needs to be applied such as ‘Least-Squares adjustment’, for instance, which turns out to be optimal under a wide range of criteria. In this context, the matrix connecting the parameters with the observations is considered fully known, and the parameter vector is considered fully unknown. This, however, is not always the reality. Therefore, two modifications of the GMM have been considered, in particular. First, ‘stochastic prior information’ (p. i.) was added on the parameters, thereby creating the – still linear – Random Effects Model (REM) where the optimal determination of the parameters (random effects) is based on ‘Least Squares collocation’, showing higher precision as long as the p. i. was adequate (Wallace test). Secondly, the coefficient matrix was allowed to contain observed elements, thus leading to the – now nonlinear – Errors-In-Variables (EIV) Model. If not using iterative linearization, the optimal estimates for the parameters would be obtained by ‘Total Least Squares adjustment’ and with generally lower, but perhaps more realistic precision. Here the two concepts are combined, thus leading to the (nonlinear) ’EIV-Model with p. i.’, where an optimal estimation (resp. prediction) technique is developed under the name of ‘Total Least-Squares collocation’. At this stage, however, the covariance matrix of the data matrix – in vector form – is still being assumed to show a Kronecker product structure.

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The Impact of Trade Credit Investment on Manufacturing Firms' Profitability: Evidence from Vietnam

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun202068040775 ◽

2020 ◽

Vol 68 (4) ◽

pp. 775-796

Author(s):

Duong Phuong Thao Pham ◽

Thi Cam Ha Huynh

Keyword(s):

Firm Performance ◽

Panel Data ◽

Least Squares ◽

Random Effects ◽

Fixed Effects ◽

Trade Credit ◽

Generalized Least Squares ◽

Manufacturing Firms ◽

Return On Assets ◽

The Impact

The aim of this study is to examine the effect that trade credit investment has on firms' profitability. The characteristics of this relationship have not been dealt with in depth for manufacturing firms. We use panel data for a total of 227 Vietnamese publicly listed manufacturing firms for the period 2005–2017. Different econometric estimation techniques such as the feasible generalized least squares, fixed effects and random effects and different calculation of firm performance such as non market-based measure (return on assets) and market-based measure (Tobin's q) are employed to validate the consistent results. The robust results confirm a statistically significant inverted U-shaped relationship between trade credit investment and profitability.

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