Joseph W McKean
Biographic Data
| ID | 5970870 |
|---|---|
| NAME | Joseph W McKean |
| GIVEN NAMES | Joseph W |
| FAMILY NAME | McKean |
| SIGNATURE | MCKEAN J W |
| AFFILIATIONS | Western Michigan University |
| ORCID | 0000-0003-1778-5564 |
| VERIFIED | Yes |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 11 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1991 |
| LATEST PUBLICATION YEAR | 2016 |
| H-INDEX | 0 |
Intimate Partner Violence Associated with Postpartum Depression, Regardless of Socioeconomic Status
Identifying Autocorrelation Generated by Various Error Processes in Interrupted Time-Series Regression Designs
Regression models used in the analysis of interrupted time-series designs assume statistically independent errors. Four methods of evaluating this assumption are the Durbin-Watson (D-W), Huitema-McKean (H-M), Box-Pierce (B-P), and Ljung-Box (L-B) tests. These tests were compared with respect to Type I error and power under a wide variety of error models and sample sizes. Although the B-P and L-B tests are portmanteau methods that incorporate info…
A Robust Method for the Analysis of Experiments With Ordered Treatment Levels
A Robust Method for the Analysis of Experiments with Ordered Treatment Levels
A robust approach for the analysis of experiments with ordered treatment levels is presented as an alternative to existing approaches such as the parametric Abelson-Tukey test for monotone alternatives and the nonparametric Terpstra-Jonckheere test. The method integrates the familiar Spearman rank-order correlation with bootstrap routines to provide magnitude of association measures, p values, and confidence intervals for magnitude of association…
Design Specification Issues in Time-Series Intervention Models
It has been recognized that the two-phase version of the interrupted time-series design can be frequently modeled using a four-parameter design matrix. There are differences across writers, however, in the details of the recommended design matrices to be used in the estimation of the four parameters of the model. Various writers imply that different methods of specifying the four-parameter design matrix all lead to the same conclusions; they do n…
A Simple and Powerful Test for Autocorrelated Errors in OLS Intervention Models
The important assumption of independent errors should be evaluated routinely in the application of interrupted time-series regression models. The two most frequently recommended tests of this assumption [Mood's runs test and the Durbin-Watson (D-W) bounds test] have several weaknesses. The former has poor small sample Type I error performance and the latter has the bothersome property that results are often declared to be "inconclusive." The test…
Autocorrelation Effects on Least-Squares Intervention Analysis of Short Time Series
Several issues regarding the effects of autocorrelated errors on Type I error in ordinary least-squares models are clarified. Although autocorrelated errors have a large effect on both omnibus F tests and tests on individual intervention effect coefficients in many applications, there are exceptions that have not been pointed out previously. It is demonstrated that under certain conditions, distortion in Type I error is far less than is predicted…
Tests for the Jackknife Autocorrelation Estimator rQ2
Two tests for the jackknife autocorrelation estimator rQ2 are evaluated. It is shown that a test based on the conventional approach for estimating the standard error of a jackknife estimator leads to unacceptable Type I error. A simple alternative approach leads to a muchmore satisfactory test that is recommended for N > 20
Reduced Bias Autocorrelation Estimation
The effectiveness of jackknife methods in reducing bias in the estimation of the lag-I autocorrelation parameter Pi was evaluated. A Monte Carlo investigation was carried out to study the empirical bias, mean-square error, and variance properties of three jackknife estimators using sample sizes that ranged from 6 through 500. The results demonstrated that these estimators are far less biased in the small sample case than are many other estimators…
Autocorrelation estimation and inference with small samples
The small sample properties of 6 autocorrelation estimators were investigated in an extensive Monte Carlo study. It was demonstrated that conventional estimators yield problems of estimation and inference in the form of(a) inconsistencies between theoretical and empirical expectations, (b) inconsistencies between theoretical and empirical error variances, and (c) dramatic differences between nominal and empirical Type I errors
Autocorrelation estimation and inference with small samples
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Autocorrelation estimation and inference with small samples
The small sample properties of 6 autocorrelation estimators were investigated in an extensive Monte Carlo study. It was demonstrated that conventional estimators yield problems of estimation and inference in the form of(a) inconsistencies between theoretical and empirical expectations, (b) inconsistencies between theoretical and empirical error variances, and (c) dramatic differences between nominal and empirical Type I errors
Autocorrelation estimation and inference with small samples
Reduced Bias Autocorrelation Estimation
The effectiveness of jackknife methods in reducing bias in the estimation of the lag-I autocorrelation parameter Pi was evaluated. A Monte Carlo investigation was carried out to study the empirical bias, mean-square error, and variance properties of three jackknife estimators using sample sizes that ranged from 6 through 500. The results demonstrated that these estimators are far less biased in the small sample case than are many other estimators…
Tests for the Jackknife Autocorrelation Estimator rQ2
Two tests for the jackknife autocorrelation estimator rQ2 are evaluated. It is shown that a test based on the conventional approach for estimating the standard error of a jackknife estimator leads to unacceptable Type I error. A simple alternative approach leads to a muchmore satisfactory test that is recommended for N > 20
Autocorrelation Effects on Least-Squares Intervention Analysis of Short Time Series
Several issues regarding the effects of autocorrelated errors on Type I error in ordinary least-squares models are clarified. Although autocorrelated errors have a large effect on both omnibus F tests and tests on individual intervention effect coefficients in many applications, there are exceptions that have not been pointed out previously. It is demonstrated that under certain conditions, distortion in Type I error is far less than is predicted…
Design Specification Issues in Time-Series Intervention Models
It has been recognized that the two-phase version of the interrupted time-series design can be frequently modeled using a four-parameter design matrix. There are differences across writers, however, in the details of the recommended design matrices to be used in the estimation of the four parameters of the model. Various writers imply that different methods of specifying the four-parameter design matrix all lead to the same conclusions; they do n…
A Simple and Powerful Test for Autocorrelated Errors in OLS Intervention Models
The important assumption of independent errors should be evaluated routinely in the application of interrupted time-series regression models. The two most frequently recommended tests of this assumption [Mood's runs test and the Durbin-Watson (D-W) bounds test] have several weaknesses. The former has poor small sample Type I error performance and the latter has the bothersome property that results are often declared to be "inconclusive." The test…
A Robust Method for the Analysis of Experiments With Ordered Treatment Levels
A Robust Method for the Analysis of Experiments with Ordered Treatment Levels
A robust approach for the analysis of experiments with ordered treatment levels is presented as an alternative to existing approaches such as the parametric Abelson-Tukey test for monotone alternatives and the nonparametric Terpstra-Jonckheere test. The method integrates the familiar Spearman rank-order correlation with bootstrap routines to provide magnitude of association measures, p values, and confidence intervals for magnitude of association…
Identifying Autocorrelation Generated by Various Error Processes in Interrupted Time-Series Regression Designs
Regression models used in the analysis of interrupted time-series designs assume statistically independent errors. Four methods of evaluating this assumption are the Durbin-Watson (D-W), Huitema-McKean (H-M), Box-Pierce (B-P), and Ljung-Box (L-B) tests. These tests were compared with respect to Type I error and power under a wide variety of error models and sample sizes. Although the B-P and L-B tests are portmanteau methods that incorporate info…
Intimate Partner Violence Associated with Postpartum Depression, Regardless of Socioeconomic Status
Mathematics (10 works) · Statistics (10 works) · Autocorrelation (8 works) · Econometrics (7 works) · Psychology (5 works) · Advanced Statistical Methods and Models (4 works) · Computer Science (4 works) · Estimation (3 works) · Forecasting Techniques and Applications (3 works) · Sample size determination (3 works)