144 research outputs found
SAQ_supplement_mat – Supplemental material for Are you 110% sure? Modeling of fractions and proportions in strategy and management research
No full textSupplemental material, SAQ_supplement_mat for Are you 110% sure? Modeling of fractions and proportions in strategy and management research by Anders R Villadsen and Jesper N Wulff in Strategic Organization</p
A multilevel Bayesian framework for predicting municipal waste generation rates
No full textPrediction of waste production is an essential part of the design and planning of waste management systems. The quality and applicability of such predictions depend heavily on model assumptions and the structure of the collected data. Ordinarily, municipal waste generation data are organized in hierarchical structures with municipal or county levels, and multilevel models can be used to generalize linear regression by directly incorporating the structure into the model. However, small amounts of data can limit the applicability of multilevel models and provide biased estimates. To cope with this problem, Bayesian estimation is often recommended as an alternative to frequentist estimation, such as least squares or maximum likelihood estimation. This paper proposes a multilevel framework under a Bayesian approach to model municipal waste generation with hierarchical data structures. Using a real-world dataset of municipal waste generation in Denmark, the predictive accuracy of multilevel models is compared to aggregated and disaggregated Bayesian models using socio-economic external variables. Results show that Bayesian multilevel models outperform the other models in prediction accuracy, based on the leave-one-out information criterion. A comparison of the Bayesian approach with its frequentist alternative shows that the Bayesian model is more conservative in coefficient estimation, with estimates shrinking to the grand mean and broader credible intervals, in contrast with narrower confidence intervals produced by the frequentist models.(c) 2021 Elsevier Ltd. All rights reserved
A bi-objective k-nearest-neighbors-based imputation method for multilevel data
No full textWe propose a bi-objective algorithm based on the k-nearest neighbors (biokNN) method to perform imputation of missing values for data with multilevel structures with continuous variables. We define the imputation method as a bi-objective minimization problem and propose a solution algorithm based on a weighted objective function. The algorithm seeks imputed values that balance the dissimilarity between the k-nearest neighbors and the observations within the same cluster. The effectiveness of the proposed method is evaluated through a simulation study, and its results are compared with those of eight benchmark imputation methods. The simulation study is based on the generation of datasets with a varying-intercept-varying-slope multilevel model, and the results are compared both by using well-known accuracy metrics and by estimating the bias of the estimates after inference has been performed. Based on the simulation, the effects of different configurations of multilevel datasets are tested, including the number of clusters, their size, their similarity, the percentage of missing values, and the effect of imbalanced clusters. The results show that the proposed method outperforms the benchmark methods, especially in cases with high intraclass correlation. A comparison of fitted linear multilevel regression models shows that our method can also reduce the bias of the estimates and the coefficient of determination. Finally, the method is tested on three commonly used machine learning datasets and shows better accuracy in most cases compared with the benchmark methods
Exploring the Relevance of Two-Part Models in Innovation Research
No full textReference list for studies included in the literature review in Pedersen, C., Villadsen, A. R., Wulff, J. N., “Exploring the Relevance of Two-Part Models in Innovation Research: Towards a Better Understanding of Innovation Sales”. In: International Journal of Innovation Management (2024), in–press
Generalized two-part fractional regression with cmp
No full textResearchers who model fractional dependent variables often need to consider whether their data were generated by a two-part process. Two-part mod- els are ideal for modeling two-part processes because they allow us to model the participation and magnitude decisions separately. While community-contributed commands currently facilitate estimation of two-part models, no specialized com- mand exists for fitting two-part models with process dependency. In this article, I describe generalized two-part fractional regression, which allows for dependency between models’ parts. I show how this model can be fit using the community- contributed cmp command (Roodman, 2011, Stata Journal 11: 159–206). I use a data example on the financial leverage of firms to illustrate how cmp can be used to fit generalized two-part fractional regression. Furthermore, I show how to obtain predicted values of the fractional dependent variable and marginal effects that are useful for model interpretation. Finally, I show how to compute model fit statistics and perform the RESET test, which are useful for model evaluation
Hic Sunt Dracones: On the Risks of Comparing the ITCV With Control Variable Correlations
No full textThis repository hosts all the code to replicate the analyses and results presented in the article "Hic Sunt Dracones: On the Risks of Comparing the ITCV With Control Variable Correlations", published in the Journal of Management
Hic Sunt Dracones: On the Risks of Comparing the ITCV With Control Variable Correlations
No full textThis repository hosts all the code to replicate the analyses and results presented in the article "Hic Sunt Dracones: On the Risks of Comparing the ITCV With Control Variable Correlations", published in the Journal of Management
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