Recent research shows that forecasting ability is an organizational distinctive competence. We propose and test a model accounting for interfirm differences in forecasting ability. After controlling for reciprocal effects, we find that two principal firm-level factors (i.e., organizational illusion of control and organizational attention) influence both bias and magnitude of errors in estimates. High organizational illusion of control increases positive forecast bias. As for organizational attention, higher relative investments in market information appear to reduce positive forecast bias and magnitude of errors; they also moderate forecast bias due to illusion of control. Finally, higher relative investments in employee capability increase both negative forecast bias and, unexpectedly, magnitude of errors for the majority of observed cases.

Predicting a firm's forecasting ability: The roles of organizational illusion of control and organizational attention

The authors use a logistic regression model, a type of generalized linear model (GLM), to represent the functional relationship between the binary response and explanatory variables, which are assumed random with specified in-control distributions.

Profile Monitoring with Binary Data and Random Predictors

https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/asl2.569

Changes and trends in maximum, minimum and mean temperature series in Iran

In this expository paper, we review methods for monitoring medical outcomes with a focus on surgical quality. We discuss the importance and role of risk adjustment. We give the advantages and disad...

The Monitoring and Improvement of Surgical-Outcome Quality

Success and Pitfalls of Information Technology Management

Empirical likelihood ratios applied to goodness-of-fit tests based on sample entropy

The paper considers generalized maximum likelihood asymptotic power one tests which aim to detect a change point in logistic regression when the alternative specifies that a change occurred in parameters of the model. A guaranteed non-asymptotic upper bound for the significance level of each of the tests is presented. For cases in which the test supports the conclusion that there was a change point, we propose a maximum likelihood estimator of that point and present results regarding the asymptotic properties of the estimator. An important field of application of this approach is occupational medicine, where for a lot chemical compounds and other agents, so-called threshold limit values (or TLVs) are specified. We demonstrate applications of the test and the maximum likelihood estimation of the change point using an actual problem that was encountered with real data.

Change point problems in the model of logistic regression

Powerful entropy-based tests for normality, uniformity and exponentiality have been well addressed in the statistical literature. The density-based empirical likelihood approach improves the performance of these tests for goodness-of-fit, forming them into approximate likelihood ratios. This method is extended to develop two-sample empirical likelihood approximations to optimal parametric likelihood ratios, resulting in an efficient test based on samples entropy. The proposed and examined distribution-free two-sample test is shown to be very competitive with well-known nonparametric tests. For example, the new test has high and stable power detecting a nonconstant shift in the two-sample problem, when Wilcoxon's test may break down completely. This is partly due to the inherent structure developed within Neyman-Pearson type lemmas. The outputs of an extensive Monte Carlo analysis and real data example support our theoretical results. The Monte Carlo simulation study indicates that the proposed test compares favorably with the standard procedures, for a wide range of null and alternative distributions.

A two-sample empirical likelihood ratio test based on samples entropy

The literature displays change point detection problems in the context of one of the key issues that belong to testing statistical hypotheses. The main focus in this article is to review recent retrospective change point policies and propose new relevant procedures. Commonly, applied quality control purposes have declared statements of the change point problems. Various biostatistical and engineering applications cause consideration of an extended form of the change point problem. In this article, we consider parametric and distribution free generalized change point detection policies, attending to different contexts of optimality and robustness of the procedures. We conducted a broad Monte Carlo study to compare various parametric and nonparametric tests, also investigating a sensitivity of the change point detection policies with respect to assumptions required for correct executions of the procedures. An example based on real biomarker measurements is provided to judge our conclusions.

/pdf/retrospective-change-point-detection-from-parametric-to-3113ab6jx5.pdf

Gregory Gurevich

Papers

Empirical likelihood ratios applied to goodness-of-fit tests based on sample entropy

Change point problems in the model of logistic regression

A two-sample empirical likelihood ratio test based on samples entropy

Change point problems in the model of logistic regression

Retrospective Change Point Detection: From Parametric to Distribution Free Policies