On the least trimmed squares estimators for JS circular regression model

This paper considers the asymptotic behavior of M-estimates in a dynamic linear regression model where the errors have infinite second moments but the exogenous regressors satisfy the standard assumptions. It is shown that under certain conditions, the estimates of the parameters corresponding to the exogenous regressors are asymptotically normal and converge to the true values at the standard n−½ rate.

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Brazilian Articles in Top-Tier Dental Journals and Influence of International Collaboration on Citation Rates

Brazilian Dental Journal ◽

10.1590/0103-6440201902826 ◽

2019 ◽

Vol 30 (4) ◽

pp. 307-316 ◽

Cited By ~ 3

Author(s):

Ana Paula R Gonçalves ◽

Bruna L Porto ◽

Bruna Rodolfo ◽

Clovis M Faggion Jr ◽

Bernardo A. Agostini ◽

...

Keyword(s):

Linear Regression ◽

Regression Model ◽

International Collaboration ◽

Linear Regression Model ◽

Regression Models ◽

Dental Materials ◽

Original Research ◽

Linear Regression Models ◽

Article Type ◽

Multilevel Linear Regression

Abstract This study investigated the presence of co-authorship from Brazil in articles published in top-tier dental journals and analyzed the influence of international collaboration, article type (original research or review), and funding on citation rates. Articles published between 2015 and 2017 in 38 selected journals from 14 dental subareas were screened in Scopus. Bibliographic information, citation counts, and funding details were recorded for all articles (N=15619). Collaboration with other top-10 publishing countries in dentistry was registered. Annual citations averages (ACA) were calculated. A linear regression model assessed differences in ACA between subareas. Multilevel linear regression models evaluated the influence of article type, funding, and presence of international collaboration in ACA. Brazil was a frequent co-author of articles published in the period (top 3: USA=25.5%; Brazil=13.8%; Germany=9.2%) and the country with most publications in two subareas. The subjects with the biggest share of Brazil are Operative Dentistry/Cariology, Dental Materials, and Endodontics. Brazil was second in total citations, but fifth in citation averages per article. From the total of 2155 articles co-authored by Brazil, 74.8% had no co-authorship from other top-10 publishing countries. USA (17.8%), Italy (4.2%), and UK (3.2%) were the main co-author countries, but the main collaboration country varied between subjects. Implantology and Dental Materials were the subjects with most international co-authorship. Review articles and articles with international collaboration were associated with increased citation rates, whereas the presence of study funding did not influence the citations.

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Analysis of SF-6D Health State Utility Scores: Is Beta Regression Appropriate?

Healthcare ◽

10.3390/healthcare8040525 ◽

2020 ◽

Vol 8 (4) ◽

pp. 525

Author(s):

Samer A Kharroubi

Keyword(s):

Linear Regression ◽

Regression Model ◽

Random Effects ◽

Regression Models ◽

Mean Squared Error ◽

Health State Utility ◽

Beta Regression ◽

Health State ◽

Linear Regression Models ◽

Utility Scores

Background: Typically, modeling of health-related quality of life data is often troublesome since its distribution is positively or negatively skewed, spikes at zero or one, bounded and heteroscedasticity. Objectives: In the present paper, we aim to investigate whether Bayesian beta regression is appropriate for analyzing the SF-6D health state utility scores and respondent characteristics. Methods: A sample of 126 Lebanese members from the American University of Beirut valued 49 health states defined by the SF-6D using the standard gamble technique. Three different models were fitted for SF-6D via Bayesian Markov chain Monte Carlo (MCMC) simulation methods. These comprised a beta regression, random effects and random effects with covariates. Results from applying the three Bayesian beta regression models were reported and compared based on their predictive ability to previously used linear regression models, using mean prediction error (MPE), root mean squared error (RMSE) and deviance information criterion (DIC). Results: For the three different approaches, the beta regression model was found to perform better than the normal regression model under all criteria used. The beta regression with random effects model performs best, with MPE (0.084), RMSE (0.058) and DIC (−1621). Compared to the traditionally linear regression model, the beta regression provided better predictions of observed values in the entire learning sample and in an out-of-sample validation. Conclusions: Beta regression provides a flexible approach to modeling health state values. It also accounted for the boundedness and heteroscedasticity of the SF-6D index scores. Further research is encouraged.

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New Flexible Regression Models Generated by Gamma Random Variables with Censored Data

International Journal of Statistics and Probability ◽

10.5539/ijsp.v5n3p9 ◽

2016 ◽

Vol 5 (3) ◽

pp. 9 ◽

Cited By ~ 4

Author(s):

Elizabeth M. Hashimoto ◽

Gauss M. Cordeiro ◽

Edwin M.M. Ortega ◽

G.G. Hamedani

Keyword(s):

Regression Model ◽

Censored Data ◽

Survival Data ◽

Regression Models ◽

Empirical Distribution ◽

Real Data ◽

Maximum Likelihood Estimates ◽

Standard Normal Distribution ◽

Model Parameters ◽

Linear Regression Models

We propose and study a new log-gamma Weibull regression model. We obtain explicit expressions for the raw and incomplete moments, quantile and generating functions and mean deviations of the log-gamma Weibull distribution. We demonstrate that the new regression model can be applied to censored data since it represents a parametric family of models which includes as sub-models several widely-known regression models and therefore can be used more effectively in the analysis of survival data. We obtain the maximum likelihood estimates of the model parameters by considering censored data and evaluate local influence on the estimates of the parameters by taking different perturbation schemes. Some global-influence measurements are also investigated. Further, for different parameter settings, sample sizes and censoring percentages, various simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We demonstrate that our extended regression model is very useful to the analysis of real data and may give more realistic fits than other special regression models.

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Robust Linear Regression Models: Use of a Stable Distribution for the Response Data

Open Journal of Statistics ◽

10.4236/ojs.2013.36048 ◽

2013 ◽

Vol 03 (06) ◽

pp. 409-416 ◽

Cited By ~ 1

Author(s):

Jorge A. Achcar ◽

Angela Achcar ◽

Edson Zangiacomi Martinez

Keyword(s):

Linear Regression ◽

Regression Models ◽

Stable Distribution ◽

Linear Regression Models ◽

Response Data ◽

Robust Linear Regression

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PERILAKU KECURANGAN AKADEMIK MAHASISWA AKUNTANSI : DIMENSI FRAUD PENTAGON (STUDI KASUS PADA MAHASISWA PRODI AKUNTANSI UKRIDA)

Jurnal Akuntansi Bisnis ◽

10.30813/jab.v12i2.1786 ◽

2019 ◽

Vol 12 (2) ◽

Author(s):

Kennedy Fadersair ◽

Subagyo Subagyo

Keyword(s):

Linear Regression ◽

Regression Model ◽

Regression Models ◽

Sampling Method ◽

Accounting Students ◽

Linear Regression Models ◽

Purposive Sampling ◽

Academic Fraud

ABSTRACT: This research examined factors that influence the behaviour of student’s cheating by using the concept of fraud pentagon consisting of pressure, opportunity, rationalization, competence and arrogance. In collecting data using questionnaires with purposive sampling method. The regression model used in this study is the linear regression models with SPSS 24. Participants in this study were 122 accounting students in Faculty of Economics and Business Christian Krida Wacana University. The result of this research shows that simultaneously fraud pentagon have significant effect to student’s academic fraud behavior. Partially, pressure and competence have positive significant effect to student’s academic fraud behavior. Arrogance have negative significant effect to student’s academic fraud behavior. Rationalization and opportunity did not influence. Keyword : academic fraud, pressure, opportunity, rasionalization, capability,arrogance. ABSTRAK: Penelitian ini untuk menguji faktor-faktor yang berpengaruh terhadap perilaku kecurangan akademik mahasiswa menggunakan konsep fraud pentagon, yaitu tekanan, kesempatan, rasionalisasi, kemampuan dan arogansi. Dalam pengumpulan data menggunakan kuesioner dengan metode purposive sampling. Model regresi yang digunakan dalam penelitian ini adalah model regresi linear berganda dengan bantuan SPSS 24. Sampel Penelitian sebanyak 122 mahasiswa Program Studi Akuntansi Fakultas Ekonomi dan Bisnis Universitas Kristen Krida Wacana. Hasil penelitian ini secara simultan menunjukkan bahwa fraud pentagon beperngaruh secara signifikan. Secara parsial menunjukkan bahwa tekanan dan kemampuan berpengaruh signifikan positif terhadap perilaku kecurangan akademik. Arogansi berpengaruh signifikan negatif terhadap perilaku kecurangan akademik. Rasionalisasi dan kesempatan tidak berpengaruh signifikan. Kata kunci : kecurangan akademik, tekanan, kesempatan, rasionalisasi, kemampuan, arogansi.

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Significant non-linearity in nitrous oxide chamber data and its effect on calculated annual emissions

Biogeosciences Discussions ◽

10.5194/bgd-6-115-2009 ◽

2009 ◽

Vol 6 (1) ◽

pp. 115-141 ◽

Cited By ~ 13

Author(s):

P. C. Stolk ◽

C. M. J. Jacobs ◽

E. J. Moors ◽

A. Hensen ◽

G. L. Velthof ◽

...

Keyword(s):

Nitrous Oxide ◽

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Regression Models ◽

Goodness Of Fit ◽

Surface Fluxes ◽

Linear Regression Models ◽

Open Questions ◽

Non Linear

Abstract. Chambers are widely used to measure surface fluxes of nitrous oxide (N2O). Usually linear regression is used to calculate the fluxes from the chamber data. Non-linearity in the chamber data can result in an underestimation of the flux. Non-linear regression models are available for these data, but are not commonly used. In this study we compared the fit of linear and non-linear regression models to determine significant non-linearity in the chamber data. We assessed the influence of this significant non-linearity on the annual fluxes. For a two year dataset from an automatic chamber we calculated the fluxes with linear and non-linear regression methods. Based on the fit of the methods 32% of the data was defined significant non-linear. Significant non-linearity was not recognized by the goodness of fit of the linear regression alone. Using non-linear regression for these data and linear regression for the rest, increases the annual flux with 21% to 53% compared to the flux determined from linear regression alone. We suggest that differences this large are due to leakage through the soil. Macropores or a coarse textured soil can add to fast leakage from the chamber. Yet, also for chambers without leakage non-linearity in the chamber data is unavoidable, due to feedback from the increasing concentration in the chamber. To prevent a possibly small, but systematic underestimation of the flux, we recommend comparing the fit of a linear regression model with a non-linear regression model. The non-linear regression model should be used if the fit is significantly better. Open questions are how macropores affect chamber measurements and how optimization of chamber design can prevent this.

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Estimating causal effects in linear regression models with observational data: The instrumental variables regression model.

Psychological Methods ◽

10.1037/met0000226 ◽

2020 ◽

Vol 25 (2) ◽

pp. 243-258 ◽

Cited By ~ 5

Author(s):

Alberto Maydeu-Olivares ◽

Dexin Shi ◽

Amanda J. Fairchild

Keyword(s):

Linear Regression ◽

Regression Model ◽

Observational Data ◽

Instrumental Variables ◽

Regression Models ◽

Causal Effects ◽

Linear Regression Models

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PERILAKU KECURANGAN AKADEMIK MAHASISWA AKUNTANSI : DIMENSI FRAUD PENTAGON (STUDI KASUS PADA MAHASISWA PRODI AKUNTANSI UKRIDA)

Jurnal Akuntansi Bisnis ◽

10.30813/jab.v12i2.1774 ◽

2019 ◽

Vol 12 (2) ◽

Author(s):

Kennedy Fadersair ◽

Subagyo Subagyo

Keyword(s):

Linear Regression ◽

Regression Model ◽

Regression Models ◽

Sampling Method ◽

Accounting Students ◽

Linear Regression Models ◽

Purposive Sampling ◽

Academic Fraud

ABSTRACT: This research examined factors that influence the behaviour of student’s cheating by using the concept of fraud pentagon consisting of pressure, opportunity, rationalization, competence and arrogance. In collecting data using questionnaires with purposive sampling method. The regression model used in this study is the linear regression models with SPSS 24. Participants in this study were 122 accounting students in Faculty of Economics and Business Christian Krida Wacana University. The result of this research shows that simultaneously fraud pentagon have significant effect to student’s academic fraud behavior. Partially, pressure and competence have positive significant effect to student’s academic fraud behavior. Arrogance have negative significant effect to student’s academic fraud behavior. Rationalization and opportunity did not influence. Keyword : academic fraud, pressure, opportunity, rasionalization, capability,arrogance. ABSTRAK: Penelitian ini untuk menguji faktor-faktor yang berpengaruh terhadap perilaku kecurangan akademik mahasiswa menggunakan konsep fraud pentagon, yaitu tekanan, kesempatan, rasionalisasi, kemampuan dan arogansi. Dalam pengumpulan data menggunakan kuesioner dengan metode purposive sampling. Model regresi yang digunakan dalam penelitian ini adalah model regresi linear berganda dengan bantuan SPSS 24. Sampel Penelitian sebanyak 122 mahasiswa Program Studi Akuntansi Fakultas Ekonomi dan Bisnis Universitas Kristen Krida Wacana. Hasil penelitian ini secara simultan menunjukkan bahwa fraud pentagon beperngaruh secara signifikan. Secara parsial menunjukkan bahwa tekanan dan kemampuan berpengaruh signifikan positif terhadap perilaku kecurangan akademik. Arogansi berpengaruh signifikan negatif terhadap perilaku kecurangan akademik. Rasionalisasi dan kesempatan tidak berpengaruh signifikan. Kata kunci : kecurangan akademik, tekanan, kesempatan, rasionalisasi, kemampuan, arogansi.

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Supplemental Material for Estimating Causal Effects in Linear Regression Models With Observational Data: The Instrumental Variables Regression Model

Psychological Methods ◽

10.1037/met0000226.supp ◽

2019 ◽

Keyword(s):

Linear Regression ◽

Regression Model ◽

Observational Data ◽

Instrumental Variables ◽

Regression Models ◽

Causal Effects ◽

Linear Regression Models

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