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

Sensitivity Analysis of Simulation Models

This contribution presents an overview of sensitivity analysis of simulation models, including the estimation of gradients. It covers classic designs and their corresponding (meta)models; namely, resolution-III designs including fractional-factorial two-level designs for first-order polynomial metamodels, resolution-IV and resolution-V designs for metamodels augmented with two-factor interactions, and designs for second-degree polynomial metamodels including central composite designs. It also reviews factor screening for simulation models with very many factors, focusing on the so-called "sequential bifurcation" method. Furthermore, it reviews Kriging metamodels and their designs. It mentions that sensitivity analysis may also aim at the optimization of the simulated system, allowing multiple random simulation outputs.simulation;sensitivity analysis;gradients;screening;Kriging;optimization;Response SurfaceMethodology;Taguchi

The role of statistical methodology in simulation

statistical methods;simulation;operations research

Design and Analysis of Monte Carlo Experiments

monte carlo experiments;simulation models;mathematical analysis;sensitivity analysis;experimental design

Regression Models and Experimental Designs: A Tutorial for Simulation Analaysts

This tutorial explains the basics of linear regression models. especially low-order polynomials. and the corresponding statistical designs. namely, designs of resolution III, IV, V, and Central Composite Designs (CCDs).This tutorial assumes 'white noise', which means that the residuals of the fitted linear regression model are normally, independently, and identically distributed with zero mean.The tutorial gathers statistical results that are scattered throughout the literature on mathematical statistics, and presents these results in a form that is understandable to simulation analysts.metamodels;fractional factorial designs;Plackett-Burman designs;factor interactions;validation;cross-validation

Sensitivity analysis and related analysis: A survey of statistical techniques

This paper reviews the state of the art in five related types of analysis, namely (i) sensitivity or what-if analysis, (ii) uncertainty or risk analysis, (iii) screening, (iv) validation, and (v) optimization. The main question is: when should which type of analysis be applied; which statistical techniques may then be used? This paper distinguishes the following five stages in the analysis of a simulation model. 1) Validation: the availability of data on the real system determines which type of statistical technique to use for validation. 2) Screening: in the simulation's pilot phase the really important inputs can be identified through a novel technique, called sequential bifurcation, which uses aggregation and sequential experimentation. 3) Sensitivity analysis: the really important inputs should be This approach with its five stages implies that sensitivity analysis should precede uncertainty analysis. This paper briefly discusses several case studies for each phase.Experimental Design;Statistical Methods;Regression Analysis;Risk Analysis;Least Squares;Sensitivity Analysis;Optimization;Perturbation;statistics

Design of Experiments for Screening

The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs and systematic fractional replicate designs. Generic issues of aliasing, bias and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are discussed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modelling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared

An Individual-based Approach to Population Dynamics with Applications to Sockeye Salmon and Iteroparous Organisms

Individual-based models have been used to study the population dynamics of semelparous and iteroparous organisms. The rst model, developed for sockeye salmon ( On-corhynchus nerka), was based on the physiology of the individual and incorporated into a population model via a McKendrick-von Foerster type partial dierential equation. Cycles of population abundance historically found in the Fraser River system were recreated through model simulations. Explanations for the appearance of the cycling were investigated and tested. The results showed that density- and size-dependent mortality were not necessary for cycling to appear, however their inclusion or exclusion in combination with the type of schooling could alter the character of the periodic cycling. The use of sequential design of experiments as a method for sensitivity analysis of the model allowed for a thorough investigation of the parameter space. The approach combined standard and non-standard designs and used reverse methodology to screen for insignificant factors. The resulting sequence of designs isolated the sensitive parameters and allowed for realistic model output. The second individual-based model was used to study iteroparous reproduction strategies and population dynamics. Two population models were formulated, a set of continuous partial differential equations of the McKendrick-von Foerster type and aset of discrete matrix equations. The asymptotic relationship between the two types of models was evaluated. It was found that a lack of convergence to the steady-state age distribution can occur in discrete event reproduction models and that convergence depends on whether the ratio between the maximum age and the length of the reproductive period is rational

Screening Experiments for Simulation: A Review

This article reviews so-called screening in simulation; i.e., it examines the search for the really important factors in experiments with simulation models that have very many factors (or inputs). The article focuses on a most efficient and effec- tive screening method, namely Sequential Bifurcation. It ends with a discussion of possible topics for future research, and forty references for further study.Screening;Metamodel;Response Surface;Design