Framework For Selecting Models Research Articles

Research on the Construction of Emergency Relief Center Site Selection Model Based on Firefly Algorithm

Aiming at the problem of site selection of rescue center under multi-objective after natural disaster, this paper constructs a site selection model to minimize the freight cost and maximize the time satisfaction of disaster victims. Firstly, the site selection model framework will be constructed according to the objectives, secondly, the model framework will be set up with specific constraints, and finally, the improved firefly optimization algorithm (SGSO) will be applied to conduct virtual simulation experiments. The SGSO algorithm is only used with the theory of the set of good points for the algorithm to initialize the population, and at the same time, it combines the inverse chord function with the step convergence factor, and it designs a formula for updating the position of the individuals in the population. On the basis of passing 8 standard test functions, virtual simulation experiments are carried out, and it is concluded that the SGSO algorithm can effectively solve the multi-objective siting problem, and provide more ideas and guarantees for the future research.

Bayesian joint analysis using a semiparametric latent variable model with non-ignorable missing covariates for CHNS data

Motivated by the China Health and Nutrition Survey (CHNS) data, a semiparametric latent variable model with a Dirichlet process (DP) mixtures prior on the latent variable is proposed to jointly analyse mixed binary and continuous responses. Non-ignorable missing covariates are considered through a selection model framework where a missing covariate model and a missing data mechanism model are included. The logarithm of the pseudo-marginal likelihood (LPML) is applied for selecting the priors, and the deviance information criterion measure focusing on the missing data mechanism model only is used for selecting different missing data mechanisms. A Bayesian index of local sensitivity to non-ignorability (ISNI) is extended to explore the local sensitivity of the parameters in our model. A simulation study is carried out to examine the empirical performance of the proposed methodology. Finally, the proposed model and the ISNI index are applied to analyse the CHNS data in the motivating example.

The determinants of individual health care expenditures in the Italian region of Friuli Venezia Giulia: evidence from a hierarchical spatial model estimation

This work investigates the determinants of health care expenditures, such as drug prescriptions, inpatient care, and outpatient care, of the resident population of the Region of Friuli Venezia Giulia (Italy). The phenomenon of interest is examined here by considering a cross-sectional register-based dataset on individual expenditures exhibiting a cross-classified hierarchical structure. In fact, patients (about 1,000,000) are grouped by general practitioners and municipalities. Does the evidence in individual data analyses support the results of the micro- and macroeconomic literature? The adoption of disaggregated data allows us to disentangle the role of the micro- and macroeconomic determinants of the expenditures. Moreover, the degree of interdependence between neighbouring municipalities is measured by accounting for the spatial correlation in the error convolution. A feasible two-stage Heckit method has to be adapted to encompass the zero-inflation issue, to consider the hierarchical structure of data and to study the spatial diffusion process of the expenditures in the sample selection model framework. The main results on the determinants of health care expenditures at the macro-level are confirmed in our analysis on disaggregated data. On the contrary, however, the substitution effect, which is typically observed in aggregated data, has not been confirmed by the present research. Moreover, the selection process appears to be relevant in drug prescriptions and outpatient care expenditures and a significant spatial correlation in both the selection and the outcome equations emerges from the structure of error components.

Pattern mixture models for the analysis of repeated attempt designs.

It is not uncommon in follow-up studies to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful or not provides useful information for the purposes of assessing the missing at random (MAR) assumption and facilitating missing not at random (MNAR) modeling. This is because measurements from subjects who provide this data after multiple failed attempts may differ from those who provide the measurement after fewer attempts. This type of "continuum of resistance" to providing a measurement has hitherto been modeled in a selection model framework, where the outcome data is modeled jointly with the success or failure of the attempts given these outcomes. Here, we present a pattern mixture approach to model this type of data. We re-analyze the repeated attempt data from a trial that was previously analyzed using a selection model approach. Our pattern mixture model is more flexible and is more transparent in terms of parameter identifiability than the models that have previously been used to model repeated attempt data and allows for sensitivity analysis. We conclude that our approach to modeling this type of data provides a fully viable alternative to the more established selection model.

Venture Capital and Corporate Governance in the Newly Public Firm*

I examine the effects of venture capital backing on the corporate governance of the entrepreneurial firm at the time of transition from private to public ownership. Using a selection model framework that instruments for venture backing with variations in the supply of venture capital, I conduct three sets of tests comparing corporate governance in venture- and non–venture-backed initial public offering (IPO) firms. Venture-backed firms have lower levels of earnings management, more positive reactions to the adoption of shareholder rights agreements, and more independent board structures than similar non–venture-backed firms, consistent with better governance. These effects are not common to all pre-IPO large shareholders.

Pattern-mixture models for categorical outcomes with non-monotone missingness

Although most models for incomplete longitudinal data are formulated within the selection model framework, pattern-mixture models have gained considerable interest in recent years [R.J.A. Little, Pattern-mixture models for multivariate incomplete data, J. Am. Stat. Assoc. 88 (1993), pp. 125–134; R.J.A. Lrittle, A class of pattern-mixture models for normal incomplete data, Biometrika 81 (1994), pp. 471–483], since it is often argued that selection models, although many are identifiable, should be approached with caution, especially in the context of MNAR models [R.J. Glynn, N.M. Laird, and D.B. Rubin, Selection modeling versus mixture modeling with nonignorable nonresponse, in Drawing Inferences from Self-selected Samples, H. Wainer, ed., Springer-Verlag, New York, 1986, pp. 115–142]. In this paper, the focus is on several strategies to fit pattern-mixture models for non-monotone categorical outcomes. The issue of under-identification in pattern-mixture models is addressed through identifying restrictions. Attention will be given to the derivation of the marginal covariate effect in pattern-mixture models for non-monotone categorical data, which is less straightforward than in the case of linear models for continuous data. The techniques developed will be used to analyse data from a clinical study in psychiatry.

Venture Capital and Corporate Governance in the Newly Public Firm

I examine the effects of venture capital backing on the corporate governance of the entrepreneurial firm at the time of transition from private to public ownership. Using a selection model framework that instruments for venture backing with variations in the supply of venture capital, I conduct three sets of tests comparing corporate governance in venture- and non-venture-backed IPO firms. Venture-backed firms have lower levels of earnings management, more positive reactions to the adoption of shareholder rights agreements, and more independent board structures than similar non-venture-backed firms, consistent with better governance. These effects are not common to all pre-IPO large shareholders.

Strategies to fit pattern-mixture models.

Whereas most models for incomplete longitudinal data are formulated within the selection model framework, pattern-mixture models have gained considerable interest in recent years (Little, 1993, 1994). In this paper, we outline several strategies to fit pattern-mixture models, including the so-called identifying restrictions strategy. Multiple imputation is used to apply this strategy to realistic settings, such as quality-of-life data from a longitudinal study on metastatic breast cancer patients.

A pattern-mixture odds ratio model for incomplete categorical data

Most models for incomplete data are formulated within the selection model framework. Pattern-mixture models are increasingly seen as a viable alternative, both from an interpretational as well as from a computational point of view (Little 1993, Hogan and Laird 1997, Ekholm and Skinner 1998). Whereas most applications are either for continuous normally distributed data or for simplified categorical settings such as contingency tables, we show how a multivariate odds ratio model (Molenberghs and Lesaffre 1994, 1998) can be used to fit pattern-mixture models to repeated binary outcomes with continuous covariates. Apart from point estimation, useful methods for interval estimation are presented and data from a clinical study are analyzed to illustrate the methods.