摘要:

We compare properties of parameter estimators under Akaike information criterion (AIC) and 'consistent' AIC (CAIC) model selection in a nested sequence of open population capture-recapture models. These models consist of product multinomials, where the cell probabilities are parameterized in terms of survival ( ) and capture ( p ) i i probabilities for each time interval i . The sequence of models is derived from 'treatment' effects that might be (1) absent, model H ; (2) only acute, model H ; or (3) acute and 0 2 p chronic, lasting several time intervals, model H . Using a 35 factorial design, 1000 3 repetitions were simulated for each of 243 cases. The true number of parameters ranged from 7 to 42, and the sample size ranged from approximately 470 to 55 000 per case. We focus on the quality of the inference about the model parameters and model structure that results from the two selection criteria. We use achieved confidence interval coverage as an integrating metric to judge what constitutes a 'properly parsimonious' model, and contrast the performance of these two model selection criteria for a wide range of models, sample sizes, parameter values and study interval lengths. AIC selection resulted in models in which the parameters were estimated with relatively little bias. However, these models exhibited asymptotic sampling variances that were somewhat too small, and achieved confidence interval coverage that was somewhat below the nominal level. In contrast, CAIC-selected models were too simple, the parameter estimators were often substantially biased, the asymptotic sampling variances were substantially too small and the achieved coverage was often substantially below the nominal level. An example case illustrates a pattern: with 20 capture occasions, 300 previously unmarked animals are released at each occasion, and the survival and capture probabilities in the control group on each occasion were 0.9 and 0.8 respectively using model H . There was a strong acute treatment effect 3 on the first survival ( ) and first capture probability ( p ), and smaller, chronic effects 1 2 on the second and third survival probabilities ( and ) as well as on the second capture 2 3 probability ( p ); the sample size for each repetition was approximately 55 000. CAIC 3 selection led to a model with exactly these effects in only nine of the 1000 repetitions, compared with 467 times under AIC selection. Under CAIC selection, even the two acute effects were detected only 555 times, compared with 998 for AIC selection. AIC selection exhibited a balance between underfitted and overfitted models (270 versus 263), while CAIC tended strongly to select underfitted models. CAIC-selected models were overly parsimonious and poor as a basis for statistical inferences about important model parameters or structure. We recommend the use of the AIC and not the CAIC for analysis and inference from capture-recapture data sets.

ISSN:0266-4763    

DOI:10.1080/02664769823250

出版商:Taylor & Francis Group    

年代:1998

数据来源: Taylor

摘要:

Univariate time series models make efficient use of available historical records of electricity consumption for short-term forecasting. However, the information (expectations) provided by electricity consumers in an energy-saving survey, even though qualitative, was considered to be particularly important, because the consumers' perception of the future may take into account the changing economic conditions. Our approach to forecasting electricity consumption combines historical data with expectations of the consumers in an optimal manner, using the technique of restricted forecasts. The same technique can be applied in some other forecasting situations in which additional information-besides the historical record of a variable-is available in the form of expectations.

ISSN:0266-4763    

DOI:10.1080/02664769823269

出版商:Taylor & Francis Group    

年代:1998

数据来源: Taylor