16,489 research outputs found
Experimental designs for environmental valuation with choice-experiments: A Monte Carlo investigation
We review the practice of experimental design in the environmental economics literature concerned with choice experiments. We then contrast this with advances in the field of experimental design and present a comparison of statistical efficiency across four different experimental designs evaluated by Monte Carlo experiments. Two different situations are envisaged. First, a correct a priori knowledge of the multinomial logit specification used to derive the design and then an incorrect one. The data generating process is based on estimates from data of a real choice experiment with which preference for rural landscape attributes were studied. Results indicate the D-optimal designs are promising, especially those based on Bayesian algorithms with informative prior. However, if good a priori information is lacking, and if there is strong uncertainty about the real data generating process - conditions which are quite common in environmental valuation - then practitioners might be better off with conventional fractional designs from linear models. Under misspecification, a design of this type produces less biased estimates than its competitors
Model selection via Bayesian information capacity designs for generalised linear models
The first investigation is made of designs for screening experiments where
the response variable is approximated by a generalised linear model. A Bayesian
information capacity criterion is defined for the selection of designs that are
robust to the form of the linear predictor. For binomial data and logistic
regression, the effectiveness of these designs for screening is assessed
through simulation studies using all-subsets regression and model selection via
maximum penalised likelihood and a generalised information criterion. For
Poisson data and log-linear regression, similar assessments are made using
maximum likelihood and the Akaike information criterion for minimally-supported
designs that are constructed analytically. The results show that effective
screening, that is, high power with moderate type I error rate and false
discovery rate, can be achieved through suitable choices for the number of
design support points and experiment size. Logistic regression is shown to
present a more challenging problem than log-linear regression. Some areas for
future work are also indicated
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Minimum aberration designs for discrete choice experiments
A discrete choice experiment (DCE) is a survey method that givesinsight into individual preferences for particular attributes.Traditionally, methods for constructing DCEs focus on identifyingthe individual effect of each attribute (a main effect). However, aninteraction effect between two attributes (a two-factor interaction)better represents real-life trade-offs, and provides us a better understandingof subjects’ competing preferences. In practice it is oftenunknown which two-factor interactions are significant. To address theuncertainty, we propose the use of minimum aberration blockeddesigns to construct DCEs. Such designs maximize the number ofmodels with estimable two-factor interactions in a DCE with two-levelattributes. We further extend the minimum aberration criteria toDCEs with mixed-level attributes and develop some general theoreticalresults
Robust designs for Poisson regression models
We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given certain constraints in the methodology, it may be necessary to extend the robust designs for implementation in practical experiments. With these extensions, our methodology constructs designs which perform similarly, in terms of estimation, to current techniques, and offers the solution in a more timely manner. We further apply this analytic result to cases where uncertainty exists in the linear predictor. The application of this methodology to practical design problems such as screening experiments is explored. Given the minimal prior knowledge that is usually available when conducting such experiments, it is recommended to derive designs robust across a variety of systems. However, incorporating such uncertainty into the design process can be a computationally intense exercise. Hence, our analytic approach is explored as an alternative
D-optimal Factorial Designs under Generalized Linear Models
Generalized linear models (GLMs) have been used widely for modelling the mean
response both for discrete and continuous random variables with an emphasis on
categorical response. Recently Yang, Mandal and Majumdar (2013) considered full
factorial and fractional factorial locally D-optimal designs for binary
response and two-level experimental factors. In this paper, we extend their
results to a general setup with response belonging to a single-parameter
exponential family and for multi-level predictors.Comment: 16 pages, 1 figur
Designs efficiency for non-market valuation with choice modelling: how to measure it, what to report and why
We review the basic principles for the evaluation of design efficiency in discrete choice modelling with a focus on efficiency of WTP estimates from the multinomial logit model. The discussion is developed under the realistic assumption that researchers can plausibly define a prior on the utility coefficients. Some new measures of design performance in applied studies are proposed and their rationale discussed. An empirical example based on the generation and comparison of fifteen separate designs from a common set of assumptions illustrates the relevant considerations to the context of non-market valuation, with particular emphasis placed on C-efficiency. Conclusions are drawn for the practice of reporting in non-market valuation and for future work on design research
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