We develop a non-parametric cost function using generalized additive models and demonstrate how to test for input separability. Our empirical example focuses on Canadian cable television (CATV) provision. We estimate a new non-parametric cost function for this industry using financial and operating data collected between 1990 and 1996. This period is of particular importance from a policy persp...

Single-site recreation demand and dichotomous choice contingent valuation analyses are typically conducted by implementing models containing strong parametric assumptions, which are rarely underpinned by theoretical arguments. This work illustrates how these assumptions can be relaxed and the estimation conducted semiparametrically by using generalized additive models (GAMs). This approach dire...

This paper studies agent-based modelling of hazards in Air Traffic Management (ATM). The study adopts a previously established large database of hazards in current and future ATM as point of departure, and explores to what extent agent-based model constructs are able to model these hazards. The agentbased modelling study is organized in three phases. During the first phase existing agent-based ...

Many papers have investigated how personal characteristics and environmental variables affect welfare durations of unmarried mothers. This paper estimates proportional hazard models for welfare durations that allow for either fixed state or fixed labor market area effects. Conditioning on residence location by fixed effects can limit the impact of three types of potential bias. (1) Estimates of...

When fitting the Cox proportional hazards model with missing covariates, it is inefficient to exclude observations with missing values in the analysis. Furthermore, if the missing-data mechanism is not Missing Completely At Random (MCAR), it may lead to biased parameter estimation. Many approaches have been suggested to handle the Cox proportional hazards model when covariates are sometimes mis...

This article concerns the statistical modelling of emergency service response times. We apply advanced methods from spatial survival analysis to deliver inference for data collected by the London Fire Brigade on response times to reported dwelling fires. Existing approaches to the analysis of these data have been mainly descriptive; we describe and demonstrate the advantages of a more sophistic...

This paper proposes a discrete-time hazard regression approach based on the relation between hazard rate models and excess over threshold models, which are frequently encountered in extreme value modelling. The proposed duration model employs a exible link function and incorporates the grouped-duration analogue of the wellknown Cox proportional hazards model and the proportional odds model as s...

Correlated survival times can be modelled by introducing a random effect, or frailty component, into the hazard function. For multivariate survival data we extend a non-PH model, the generalized time-dependent logistic survival model, to include random effects. The hierarchical-likelihood procedure, which obviates the need for marginalization over the random effect distribution, is derived for ...

data envelopment analysis (dea) models with interval inputs and outputs have been rarely discussed in dea literature. this paper, using the enhanced russell measurement proposes an extended model which permits the presence of interval scale variables which can take both negative and positive values. the model is compared with most well-known dea models of which include the ccr model, the bcc mo...

First hitting time models are a technique of modeling a stochastic process as it approaches or avoids a boundary, also known as a threshold. The process itself may be unobservable, making this a difficult problem. Regression techniques, however, can be employed to model the data as it compares to the threshold, creating a class of first hitting time models called threshold regression models. Su...