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Full-Text Articles in Social and Behavioral Sciences
Detecting Careless Cases In Practice Tests, Steven Nydick
Detecting Careless Cases In Practice Tests, Steven Nydick
Chinese/English Journal of Educational Measurement and Evaluation | 教育测量与评估双语期刊
In this paper, we present a novel method for detecting careless responses in a low-stakes practice exam using machine learning models. Rather than classifying test-taker responses as careless based on model fit statistics or knowledge of truth, we built a model to predict significant changes in test scores between a practice test and an official test based on attributes of practice test items. We extracted features from practice test items using hypotheses about how careless test takers respond to items and cross-validated model performance to optimize out-of-sample predictions and reduce heteroscedasticity when predicting the closest official test. All analyses use …
Comparing Means Under Heteroscedasticity And Nonnormality: Further Exploring Robust Means Modeling, Alyssa Counsell, Robert Philip Chalmers, Robert A. Cribbie
Comparing Means Under Heteroscedasticity And Nonnormality: Further Exploring Robust Means Modeling, Alyssa Counsell, Robert Philip Chalmers, Robert A. Cribbie
Journal of Modern Applied Statistical Methods
Comparing the means of independent groups is a concern when the assumptions of normality and variance homogeneity are violated. Robust means modeling (RMM) was proposed as an alternative to ANOVA-type procedures when the assumptions of normality and variance homogeneity are violated. The purpose of this study is to compare the Type I error and power rates of RMM to the trimmed Welch procedure. A Monte Carlo study was used to investigate RMM and the trimmed Welch procedure under several conditions of nonnormality and variance heterogeneity. The results suggest that the trimmed Welch provides a better balance of Type I error …
Nonlinear Cointegrating Power Function Regression With Endogeneity, Zhishui Hu, Peter C.B. Phillips, Qiying Wang
Nonlinear Cointegrating Power Function Regression With Endogeneity, Zhishui Hu, Peter C.B. Phillips, Qiying Wang
Cowles Foundation Discussion Papers
This paper develops an asymptotic theory for nonlinear cointegrating power function regression. The framework extends earlier work on the deterministic trend case and allows for both endogeneity and heteroskedasticity, which makes the models and inferential methods relevant to many empirical economic and financial applications, including predictive regression. Accompanying the asymptotic theory of nonlinear regression, the paper establishes some new results on weak convergence to stochastic integrals that go beyond the usual semi-martingale structure and considerably extend existing limit theory, complementing other recent findings on stochastic integral asymptotics. The paper also provides a general framework for extremum estimation limit theory that …
On The Conditional And Unconditional Type I Error Rates And Power Of Tests In Linear Models With Heteroscedastic Errors, Patrick J. Rosopa, Alice M. Brawley, Theresa P. Atkinson, Stephen A. Robertson
On The Conditional And Unconditional Type I Error Rates And Power Of Tests In Linear Models With Heteroscedastic Errors, Patrick J. Rosopa, Alice M. Brawley, Theresa P. Atkinson, Stephen A. Robertson
Journal of Modern Applied Statistical Methods
Preliminary tests for homoscedasticity may be unnecessary in general linear models. Based on Monte Carlo simulations, results suggest that when testing for differences between independent slopes, the unconditional use of weighted least squares regression and HC4 regression performed the best across a wide range of conditions.
Robust Ancova, Curvature, And The Curse Of Dimensionality, Rand Wilcox
Robust Ancova, Curvature, And The Curse Of Dimensionality, Rand Wilcox
Journal of Modern Applied Statistical Methods
There is a substantial collection of robust analysis of covariance (ANCOVA) methods that effectively deals with non-normality, unequal population slope parameters, outliers, and heteroscedasticity. Some are based on the usual linear model and others are based on smoothers (nonparametric regression estimators). However, extant results are limited to one or two covariates. A minor goal here is to extend a recently-proposed method, based on the usual linear model, to situations where there are up to six covariates. The usual linear model might provide a poor approximation of the true regression surface. The main goal is to suggest a method, based on …
The Regression Smoother Lowess: A Confidence Band That Allows Heteroscedasticity And Has Some Specified Simultaneous Probability Coverage, Rand Wilcox
Journal of Modern Applied Statistical Methods
Many nonparametric regression estimators (smoothers) have been proposed that provide a more flexible method for estimating the true regression line compared to using some of the more obvious parametric models. A basic goal when using any smoother is computing a confidence band for the true regression line. Let M(Y|X) be some conditional measure of location associated with the random variable Y, given X and let x be some specific value of the covariate. When using the LOWESS estimator, an extant method that assumes homoscedasticity can be used to compute a confidence interval for M(Y|X = x). A trivial way of …
Accounting For Locational, Temporal, And Physical Similarity Of Residential Sales In Mass Appraisal Modeling: The Development And Application Of Geographically, Temporally, And Characteristically Weighted Regression, Paul E. Bidanset, Michael Mccord, John R. Lombard, Peadar Davis, William J. Mccluskey
Accounting For Locational, Temporal, And Physical Similarity Of Residential Sales In Mass Appraisal Modeling: The Development And Application Of Geographically, Temporally, And Characteristically Weighted Regression, Paul E. Bidanset, Michael Mccord, John R. Lombard, Peadar Davis, William J. Mccluskey
School of Public Service Faculty Publications
Geographically weighted regression (GWR) has been recognized in the assessment community as a viable automated valuation model (AVM) to help overcome, at least in part, modeling hurdles associated with location, such as spatial heterogeneity and spatial autocorrelation of error terms. Although previous researchers have adjusted the GWR weights matrix to also weight by time of sale or by structural similarity of properties in AVMs, the research described in this paper is the first that has done so by all three dimensions (i.e., location, structural similarity, and time of sale) simultaneously. Using 24 years of single-family residential sales in Fairfax, Virginia, …
Modelling Volatility Of The Exchange Rate Of The Naira To Major Currencies, Reuben O. David, Hussaini G. Dikko, Shehu U. Gulumbe
Modelling Volatility Of The Exchange Rate Of The Naira To Major Currencies, Reuben O. David, Hussaini G. Dikko, Shehu U. Gulumbe
CBN Journal of Applied Statistics (JAS)
The exchange rate between the Naira and other currencies has continued to witness variability with depreciation. This variability makes it difficult to predict returns. Against this background, this paper examines the naira exchange rate vis-a-vis four other currencies. The impact of exogenous variables in modelling volatility is considered using both the GARCH (1,1) and its asymmetric variants. Three of the four returns series showed heteroscedasticity. The results of the fitted models indicate that the majority of the parameters are significant and that volatility is quite persistent. Furthermore, the results of the asymmetric model indicate different impacts for both negative and …
Improved Ridge Estimator In Linear Regression With Multicollinearity, Heteroscedastic Errors And Outliers, Ashok Vithoba Dorugade
Improved Ridge Estimator In Linear Regression With Multicollinearity, Heteroscedastic Errors And Outliers, Ashok Vithoba Dorugade
Journal of Modern Applied Statistical Methods
This paper introduces a new estimator, of ridge parameter k for ridge regression and then evaluated by Monte Carlo simulation. We examine the performance of the proposed estimators compared with other well-known estimators for the model with heteroscedastics and/or correlated errors, outlier observations, non-normal errors and suffer from the problem of multicollinearity. It is shown that proposed estimators have a smaller MSE than the ordinary least squared estimator (LS), Hoerl and Kennard (1970) estimator (RR), jackknifed modified ridge (JMR) estimator, and Jackknifed Ridge M‑estimator (JRM).
Inferences About The Skipped Correlation Coefficient: Dealing With Heteroscedasticity And Non-Normality, Rand Wilcox
Inferences About The Skipped Correlation Coefficient: Dealing With Heteroscedasticity And Non-Normality, Rand Wilcox
Journal of Modern Applied Statistical Methods
A common goal is testing the hypothesis that Pearson’s correlation is zero and typically this is done based on Student’s T test. There are, however, several well-known concerns. First, Student’s T is sensitive to heteroscedasticity. That is, when it rejects, it is reasonable to conclude that there is dependence, but in terms of making a decision about the strength of the association, it is unsatisfactory. Second, Pearson’s correlation is not robust: it can poorly reflect the strength of the association. Even a single outlier can have a tremendous impact on the usual estimate of Pearson’s correlation, which can result in …
Share-Price-Changes-Volume Relation On The Singapore Equity Market, David K. C. Lee, Mohamed Ariff
Share-Price-Changes-Volume Relation On The Singapore Equity Market, David K. C. Lee, Mohamed Ariff
David LEE Kuo Chuen
A critical review of the literature on security-price-changes-volume research suggests that the published studies in the United States and one each in Hong Kong and Japan have largely ignored the impacts on the results from autocorrelation, non-normality of distributions, heteroscedasticity and non-linear functional forms. Therefore, the reported findings are not robust. In testing for this relation from a small sample of continuously traded shares in the Singapore share market, we find that consistent results may not be obtained because of violations of basic test conditions. A task that remains is an application of alternative test models with data transformation using …
Lm Tests Of Spatial Dependence Based On Bootstrap Critical Values, Zhenlin Yang
Lm Tests Of Spatial Dependence Based On Bootstrap Critical Values, Zhenlin Yang
Research Collection School Of Economics
To test the existence of spatial dependence in an econometric model, a convenient test is the Lagrange Multiplier (LM) test. However, evidence shows that, in finite samples, the LM test referring to asymptotic critical values may suffer from the problems of size distortion and low power, which become worse with a denser spatial weight matrix. In this paper, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values of the LM statistics. Conditions for their validity are clearly laid out and formal justifications are given in general, and in details under several popular spatial LM …
Explicit Equations For Acf In Autoregressive Processes In The Presence Of Heteroscedasticity Disturbances, Samir Safi
Explicit Equations For Acf In Autoregressive Processes In The Presence Of Heteroscedasticity Disturbances, Samir Safi
Journal of Modern Applied Statistical Methods
The autocorrelation function, ACF, is an important guide to the properties of a time series. Explicit equations are derived for ACF in the presence of heteroscedasticity disturbances in pth order autoregressive, AR(p), processes. Two cases are presented: (1) when the disturbance term follows the general covariance matrix, Σ , and (2) when the diagonal elements of Σ are not all identical but σi,j = 0 ∀i ≠ j.
Level Robust Methods Based On The Least Squares Regression Estimator, Marie Ng, Rand R. Wilcox
Level Robust Methods Based On The Least Squares Regression Estimator, Marie Ng, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
Heteroscedastic consistent covariance matrix (HCCM) estimators provide ways for testing hypotheses about regression coefficients under heteroscedasticity. Recent studies have found that methods combining the HCCM-based test statistic with the wild bootstrap consistently perform better than non-bootstrap HCCM-based methods (Davidson & Flachaire, 2008; Flachaire, 2005; Godfrey, 2006). This finding is more closely examined by considering a broader range of situations which were not included in any of the previous studies. In addition, the latest version of HCCM, HC5 (Cribari-Neto, et al., 2007), is evaluated.
Adaptive Estimation Of Heteroscedastic Linear Regression Model Using Probability Weighted Moments, Faqir Muhammad, Muhammad Aslam, G.R. Pasha
Adaptive Estimation Of Heteroscedastic Linear Regression Model Using Probability Weighted Moments, Faqir Muhammad, Muhammad Aslam, G.R. Pasha
Journal of Modern Applied Statistical Methods
An adaptive estimator is presented by using probability weighted moments as weights rather than conventional estimates of variances for unknown heteroscedastic errors while estimating a heteroscedastic linear regression model. Empirical studies of the data generated by simulations for normal, uniform, and logistically distributed error terms support our proposed estimator to be quite efficient, especially for small samples.
Least Squares Percentage Regression, Chris Tofallis
Least Squares Percentage Regression, Chris Tofallis
Journal of Modern Applied Statistical Methods
In prediction, the percentage error is often felt to be more meaningful than the absolute error. We therefore extend the method of least squares to deal with percentage errors, for both simple and multiple regression. Exact expressions are derived for the coefficients, and we show how such models can be estimated using standard software. When the relative error is normally distributed, least squares percentage regression is shown to provide maximum likelihood estimates. The multiplicative error model is linked to least squares percentage regression in the same way that the standard additive error model is linked to ordinary least squares regression.
A Corrected Plug-In Method For Quantile Interval Construction Through A Transformed Regression, Zhenlin Yang, Yiu Kuen Tse
A Corrected Plug-In Method For Quantile Interval Construction Through A Transformed Regression, Zhenlin Yang, Yiu Kuen Tse
Research Collection School Of Economics
We propose a corrected plug-in method for constructing confidence intervals of the conditional quantiles of an original response variable through a transformed regression with heteroscedastic errors. The interval is easy to compute. Factors affecting the magnitude of the correction are examined analytically through the special case of Box-Cox regression. Monte Carlo simulations show that the new method works well in general and is superior over the commonly used delta method and the quantile regression method. An empirical application is presented. [PUBLICATION ABSTRACT]
The Effects Of Heteroscedasticity On Tests Of Equivalence, Jamie A. Gruman, Robert A. Cribbie, Chantal A. Arpin-Cribbie
The Effects Of Heteroscedasticity On Tests Of Equivalence, Jamie A. Gruman, Robert A. Cribbie, Chantal A. Arpin-Cribbie
Journal of Modern Applied Statistical Methods
Tests of equivalence, which are designed to assess the similarity of group means, are becoming more popular, yet very little is known about the statistical properties of these tests. Monte Carlo methods are used to compare the test of equivalence proposed by Schuirmann with modified tests of equivalence that incorporate a heteroscedastic error term. It was found that the latter were more accurate than the Schuirmann test in detecting equivalence when sample sizes and variances were unequal.
On Flexible Tests Of Independence And Homoscedasticity, Rand R. Wilcox
On Flexible Tests Of Independence And Homoscedasticity, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
Consider the nonparametric regression model Y = m(X) + τ(X)ε , where X and ε are independent random variables, ε has a mean of zero and variance σ2, τ is some unknown function used to model heteroscedasticity, and m(X) is an unknown function reflecting some conditional measure of location associated with Y, given X. Detecting dependence, by testing the hypothesis that m(X) does not vary with X, has the potential of being more sensitive to a wider range of associations compared to using Pearson's correlation. This note has two goals. The first is to point …
A Comparison Of Ordinary Least Squares, Weighted Least Squares, And Other Procedures When Testing For The Equality Of Regression, Patrick J. Rosopa
A Comparison Of Ordinary Least Squares, Weighted Least Squares, And Other Procedures When Testing For The Equality Of Regression, Patrick J. Rosopa
Electronic Theses and Dissertations
When testing for the equality of regression slopes based on ordinary least squares (OLS) estimation, extant research has shown that the standard F performs poorly when the critical assumption of homoscedasticity is violated, resulting in increased Type I error rates and reduced statistical power (Box, 1954; DeShon & Alexander, 1996; Wilcox, 1997). Overton (2001) recommended weighted least squares estimation, demonstrating that it outperformed OLS and performed comparably to various statistical approximations. However, Overton's method was limited to two groups. In this study, a generalization of Overton's method is described. Then, using a Monte Carlo simulation, its performance was compared to …
Jmasm8: Using Sas To Perform Two-Way Analysis Of Variance Under Variance Heterogeneity, Scott J. Richter, Mark E. Payton
Jmasm8: Using Sas To Perform Two-Way Analysis Of Variance Under Variance Heterogeneity, Scott J. Richter, Mark E. Payton
Journal of Modern Applied Statistical Methods
We present SAS code to implement the method proposed by Brunner et al. (1997) for performing two-way analysis of variance under variance heterogeneity.
Tests Of Functional Form And Heteroscedasticity, Zhenlin Yang, Yiu Kuen Tse
Tests Of Functional Form And Heteroscedasticity, Zhenlin Yang, Yiu Kuen Tse
Research Collection School Of Economics
This paper considers tests of misspecification in a heteroscedastic transformation model. We derive Lagrange multiplier (LM) statistics for (i) testing functional form and heteroscedasticity jointly, (ii) testing functional form in the presence of heteroscedasticity, and (iii) testing heteroscedasticity in the presence of data transformation. We present LM statistics based on the expected information matrix. For cases (i) and (ii), this is done assuming the Box-Cox transformation. For case (iii), the test does not depend on whether the functional form is estimated or pre-specified. Small-sample properties of the tests are assessed by Monte Carlo simulation, and comparisons are made with the …
A Corrected Plug-In Method For The Quantile Confidence Interval Of A Transformed Regression, Zhenlin Yang, Yiu Kuen Tse
A Corrected Plug-In Method For The Quantile Confidence Interval Of A Transformed Regression, Zhenlin Yang, Yiu Kuen Tse
Research Collection School Of Economics
In this paper we propose an analytically corrected plug-in method for constructing confidence intervals of the conditional quantiles of a response variable with data transformation. The method can be applied to (i) a general conditional regression quantile, (ii) a general monotonic transformation, and (iii) a transformation model with heteroscedastic errors. Our results extend those in Yang (2002a), in which the median of a response variable under the Box-Cox transformation with homoscedastic errors was considered. A Monte Carlo experiment is conducted to compare the performance of the corrected plug-in method, the plug-in method and the delta method. The corrected plug-in method …
On The Proper Use Of Box-Cox Transformation Method: A Note On A Taguchi Case Study, Zhenlin Yang
On The Proper Use Of Box-Cox Transformation Method: A Note On A Taguchi Case Study, Zhenlin Yang
Research Collection School Of Economics
In studying the role of transformation in the Taguchi method, Logothetis (1990) analyzed the data from a plasma etching process and concluded that the Box-Cox method can induce a mean bias in the variability performance measure which can inhibit the production of clearcut results. This paper points out that the above conclusion is in part due to an inappmpriate application of the Box-Cox method where the transformation parameter is determined from one model but the analysis is done on the other. Further, it may not be appropriate to state that Box-Cox method induces a mean bias, but rather that there …
Share-Price-Changes-Volume Relation On The Singapore Equity Market, David K. C. Lee, Mohamed Ariff
Share-Price-Changes-Volume Relation On The Singapore Equity Market, David K. C. Lee, Mohamed Ariff
Research Collection Lee Kong Chian School Of Business
A critical review of the literature on security-price-changes-volume research suggests that the published studies in the United States and one each in Hong Kong and Japan have largely ignored the impacts on the results from autocorrelation, non-normality of distributions, heteroscedasticity and non-linear functional forms. Therefore, the reported findings are not robust. In testing for this relation from a small sample of continuously traded shares in the Singapore share market, we find that consistent results may not be obtained because of violations of basic test conditions. A task that remains is an application of alternative test models with data transformation using …