Sound the Alarm: Limitations of Liability in Alarm Service Contracts

Home and business owners increasingly rely on alarm systems to protect against theft and property damage. When a burglary or fire occurs and an alarm service customer discovers that the alarm company negligently failed to call the police or fire department, the customer understandably would expect redress for the company’s failure to provide its service. Many customers would be surprised, though, to discover that an alarm company’s liability is often contractually limited to a relatively token amount unrelated to the cost of the service, even when the alarm company is negligent. Some states view these limitations of liability as exculpatory clauses and determine their enforceability based on whether they are unconscionable or violate public policy. Other states view them as liquidated damages and apply a penalty test to determine their enforceability. This Note addresses the differences between these two approaches in the context of the unique remedy difficulties inherent in alarm service contracts. This Note then argues that the prevailing policy rationales for enforcing alarm service provisions that limit a party’s liability for its own negligence are misguided and advocates that these provisions should not be enforced as a matter of public policy

A comparison of parameter covariance estimation methods for item response models in an expectation-maximization framework

The Expectation-Maximization (EM) algorithm is a method for finding the maximum likelihood estimate of a model in the presence of missing data. Unfortunately, EM does not produce a parameter covariance matrix for standard errors. Both Oakes and Supplemented EM are methods for obtaining the parameter covariance matrix. SEM was discovered in 1991 and is implemented in both opensource and commercial item response model estimation software. Oakes, a more recent method discovered in 1999, had not been implemented in item response model software until now. Convergence properties, accuracy, and elapsed time of Oakes and Supplemental EM family algorithms are compared for a diverse selection IFA models. Oakes exhibits the best accuracy and elapsed time among algorithms compared. We recommend that Oakes be made available in item response model estimation software