Using spectral analysis and multinomial logit regression to explain households’ choice patterns
Tóm tắt
Many methods are available to analyze rank-ordered data. We used spectral analysis to identify the most preferred option of Formosan subterranean termites (FSTs) control as ranked by Louisiana homeowners. Respondents were asked to rank four termite control methods from the most preferred option to the least preferred option. Spectral analysis of complete ranked data indicated that the most preferred FST control choice is a relatively cheap ($0.13/square foot) option of a liquid treatment. Similarly, analysis indicated that liquid and bait treatments are the two most desired control choices. Multinomial logit analysis indicated that survey location, household pre-tax income, and knowledge of FSTs determined Louisiana homeowners’ ranking pattern choices.
Tài liệu tham khảo
Beggs S, Cardell S, Hausman J (1981) Assessing the potential demand for electric cars. J Econom 17(1): 1–19
Benter W (1994) Computer based horse race handicapping and wagering systems: a report. In: Hausch DB, Lo VSY, Ziemba WT (eds) Efficiency of racetrack betting markets. Harcourt Brace, San Diego, pp 183–198
Chapman RG, Staelin R (1982) Exploiting rank ordered choice set data within the stochastic utility model. J Market Res 19(3): 288–301
Critchlow D (1985) Metric methods for analyzing partially ranked data. Springer-Verlag, New York
Diaconis P (1988) Group representations in probability and statistics, vol 11. Lecture notes—monograph series. Institute of Mathematical Statistics, Hayward
Diaconis P (1989) A generalization of spectral analysis with application to ranked data. Ann Stat 17(3): 949–979
Dillman D (2000) Mail and internet surveys: the tailored design method. Wiley, New York
Eiswerth ME, Johnson WS (2002) Managing nonindigenous invasive species: insights from dynamic analysis. Environ Resour Econ 23(3): 319–342
Eriksson NK (2006) Algebraic combinatorics for computational biology. Dissertation, University of California, Berkeley
Fernandez L (2007) Maritime trade and migratory species management to protect biodiversity. Environ Resour Econ 38(2): 165–188
Gormley IC, Murphy TB (2008) A mixture of experts model for rank data with applications in election studies. Ann Appl Stat 2(4): 1452–1477
Gormley IC, Murphy B (2010) Clustering ranked preference data using sociodemographic covariates. In: Hess S, Daly A (eds) Choice modeling: the state-of-the-art and the state-of-practice, 1st edn. Emerald, UK, pp 543–569
Greene W, Hensher D (2010) Modeling ordered choices: a primer, 1st edn. Cambridge University Press, New York
Hannan EJ (1965) Group representations and applied probability. J Appl Probab 2(1): 1–68
Iwasaki M (1992) Spectral analysis of multivariate binary data. J Jpn Stat Soc 22: 45–65
Jackson JE, Lawton WH (1969) Comparison of ANOVA and harmonic components of variance. Technometrics 11(1): 75–90
Johnston RJ, Roheim CA (2006) A battle of taste and environmental convictions for ecolabeled seafood: a contingent ranking experiment. J Agric Resour Econ 31(2): 283–300
Kidwell P, Lebanon G, Cleveland W (2008) Visualizing incomplete and partially ranked data. IEEE Trans Vis Comput Graph 14(6): 1356–1363
Lawson BL, Orrison ME (2002) Analyzing voting from a new perspective: applying spectral analysis to the U.S. supreme court. Paper presented at the American Political Science Association, Boston, MA
Lax AR, Osbrink WLA (2003) United States Department of Agriculture-Agriculture Research Service research on targeted management of the Formosan subterranean termite Coptotermes formosanus Shiraki (Isoptera: Rhinotermitidae). Pest Manag Sci 59(6–7): 788–800
Lee PH, Yu PLH (2010) Distance-based tree models for ranking data. Comput Stat Data Anal 54(6): 1672–1682
Mallows CL (1957) Non-null ranking models. I. Biometrika 44(1/2): 114–130
Marden J (1995) Analyzing and modeling rank data, 1st edn. Chapman & Hall, New York
McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (eds) Frontiers in econometrics. Academic Press, New York, pp 105–142
Paudel KP, Dunn MA, Bhandari D, Vlosky RP, Guidry KM (2007) Alternative methods to analyze the rank ordered data: a case of invasive species control. Nat Resour Model 20(3): 451–471
Pedrotti L, Rensi S, Zaninotto E (2006) Shortlisting by incomplete descriptions: the power of combination. ROCK working paper. University of Trento, Italy
Pimentel D, Zuniga R, Morrison D (2005) Update on the environmental and economic costs associated with alien-invasive species in the united states. Ecol Econ 52(3): 273–288
Powers DA, Xie Y (2000) Statistical methods for categorical data analysis. Academic Press, New York
Scheirer CJ, Ray WS, Hare N (1976) The analysis of ranked data derived from completely randomized factorial designs. Biometrics 32(2): 429–434
Serre JP (1977) Representations of groups; finite groups. Springer-Verlag, New York
Su N-Y, Scheffrahn RH (1987) Current status of the Formosan subterranean termite in Florida. In: Tamashiro M, Su N-Y (eds) Biology and control of the Formosan subterranean termite. College of Tropical Agriculture and Human Resources, University of Hawaii, Honolulu, pp 27–31
Thompson GL (1993) Generalized permutation polytopes and exploratory graphical methods for ranked data. Ann Stat 21(3): 1401–1430
Wu MX, Yub KF, Liub AY (2009) Estimation of variance components in the mixed effects models: a comparison between analysis of variance and spectral decomposition. J Stat Plan Inference 139: 3962–3973