Constrained parsimonious model-based clustering

A new methodology for constrained parsimonious model-based clustering is introduced, where some tuning parameter allows to control the strength of these constraints. The methodology includes the 14 parsimonious models that are often applied in model-based clustering when assuming normal components as limit cases. This is done in a natural way by filling the gap among models and providing a smooth transition among them. The methodology provides mathematically well-defined problems and is also useful to prevent us from obtaining spurious solutions. Novel information criteria are proposed to help the user in choosing parameters. The interest of the proposed methodology is illustrated through simulation studies and a real-data application on COVID data.

Why do I use parsimonious models?

Today, when reading about multicollinearity – one of the most dreaded problems in regression analysis, I came across the statement of Blanchard [8]: “Multicollinearity is God’s will, not a problem with OLS or statistical techniques in general.” Immediately, I thought:“If it is really God’s will, we should keep the problem within our grasp, but not rely on God.”

Tango: Declarative Semantics for Multiagent Communication Protocols

A flexible communication protocol is necessary to build a decentralized multiagent system whose member agents are not coupled to each other's decision making. Information-based protocol languages capture a protocol in terms of causality and integrity constraints based on the information exchanged by the agents. Thus, they enable highly flexible enactments in which the agents proceed asynchronously and messages may be arbitrarily reordered. However, the existing semantics for such languages can produce a large number of protocol enactments, which makes verification of a protocol property intractable. This paper formulates a protocol semantics declaratively via inference rules that determine when a message emission or reception becomes enabled during an enactment, and its effect on the local state of an agent. The semantics enables heuristics for determining when alternative extensions of a current enactment would be equivalent, thereby helping produce parsimonious models and yielding improved protocol verification methods.

CT-Detected Subsolid Nodules: A Predictor of Lung Cancer Development at Another Location?

The purpose of this case–cohort study was to investigate whether the frequency and computed tomography (CT) features of pulmonary nodules posed a risk for the future development of lung cancer (LC) at a different location. Patients scanned between 2004 and 2012 at two Dutch academic hospitals were cross-linked with the Dutch Cancer Registry. All patients who were diagnosed with LC by 2014 and a random selection of LC-free patients were considered. LC patients who were determined to be LC-free at the time of the scan and all LC-free patients with an adequate scan were included. The nodule count and types (solid, part-solid, ground-glass, and perifissural) were recorded per scan. Age, sex, and other CT measures were included to control for confounding factors. The cohort included 163 LC patients and 1178 LC-free patients. Cox regression revealed that the number of ground-glass nodules and part-solid nodules present were positively correlated to future LC risk. The area under the receiver operating curve of parsimonious models with and without nodule type information were 0.827 and 0.802, respectively. The presence of subsolid nodules in a clinical setting may be a risk factor for future LC development in another pulmonary location in a dose-dependent manner. Replication of the results in screening cohorts is required for maximum utility of these findings.

A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions

We shed light on computational challenges when fitting the Nelson-Siegel, Bliss and Svensson parsimonious yield curve models to observed US Treasury securities with maturities up to 30 years. As model parameters have a specific financial meaning, the stability of their estimated values over time becomes relevant when their dynamic behavior is interpreted in risk-return models. Our study is the first in the literature that compares the stability of estimated model parameters among different parsimonious models and for different approaches for predefining initial parameter values. We find that the Nelson-Siegel parameter estimates are more stable and conserve their intrinsic economical interpretation. Results reveal in addition the patterns of confounding effects in the Svensson model. To obtain the most stable and intuitive parameter estimates over time, we recommend the use of the Nelson-Siegel model by taking initial parameter values derived from the observed yields. The implications of excluding Treasury bills, constraining parameters and reducing clusters across time to maturity are also investigated.

Bayesian approaches to variable selection: a comparative study from practical perspectives

In many clinical studies, researchers are interested in parsimonious models that simultaneously achieve consistent variable selection and optimal prediction. The resulting parsimonious models will facilitate meaningful biological interpretation and scientific findings. Variable selection via Bayesian inference has been receiving significant advancement in recent years. Despite its increasing popularity, there is limited practical guidance for implementing these Bayesian approaches and evaluating their comparative performance in clinical datasets. In this paper, we review several commonly used Bayesian approaches to variable selection, with emphasis on application and implementation through R software. These approaches can be roughly categorized into four classes: namely the Bayesian model selection, spike-and-slab priors, shrinkage priors, and the hybrid of both. To evaluate their variable selection performance under various scenarios, we compare these four classes of approaches using real and simulated datasets. These results provide practical guidance to researchers who are interested in applying Bayesian approaches for the purpose of variable selection.

Regression models for binary response applied to data on neonatal deaths in newborns

The present study presents binary data modeling regarding 1.6% of neonatal deaths in 3,448 newborns from an epidemiological and observational study with a cross-sectional design, involving the retrospective analysis of 4,293 medical records of high-risk pregnant women followed in a gestational outpatient clinic from September 2012 to September 2017. Different symmetric and asymmetric link functions were considered by means of Bayesian inference. The support of more accurate inferences regarding the parameters of the model will provide biological interpretations that are more reliable and consistent with the reality. The model that presented, significantly, the lowest value for the deviance information criterion (DIC = 398.8), was the binomial with power logit (PL) link function, whose median posterior value estimated and significant for the parameter asymmetry was l = 0.25 (0.14;1.17). This significance is observed in all other models of the power family, however with very different values ​​and significantly higher DIC values, indicating less parsimonious models. The Bayesian methodology proved to be flexible. Additionally, the results show that such model shows an accuracy = 97.4% and area under the ROC curve AUC = 89.4% in the prediction of neonatal deaths based on the weight of children at birth. Specifically, for 2.500g, a value predicted in the medical literature for low weight, the model predicts a probability of 1.43%.

Determining the Number of Factors When Population Models Can Be Closely Approximated by Parsimonious Models

Despite the existence of many methods for determining the number of factors, none outperforms the others under every condition. This study compares traditional parallel analysis (TPA), revised parallel analysis (RPA), Kaiser’s rule, minimum average partial, sequential χ2, and sequential root mean square error of approximation, comparative fit index, and Tucker–Lewis index under a realistic scenario in behavioral studies, where researchers employ a closing–fitting parsimonious model with K factors to approximate a population model, leading to a trivial model-data misfit. Results show that while traditional and RPA both stand out when zero population-level misfits exist, the accuracy of RPA substantially deteriorates when a K-factor model can closely approximate the population. TPA is the least sensitive to trivial misfits and results in the highest accuracy across most simulation conditions. This study suggests the use of TPA for the investigated models. Results also imply that RPA requires further revision to accommodate a degree of model–data misfit that can be tolerated.

Nonlinear parsimonious forest modeling assuming normal distribution of residuals

To avoid the transformation of the dependent variable, which introduces bias when back-transformed, complex nonlinear forest models have the parameters estimated with heuristic techniques, which can supply erroneous values. The solution for accurate nonlinear models provided by Strimbu et al. (Ecosphere 8:e01945, 2017) for 11 functions (i.e., power, trigonometric, and hyperbolic) is not based on heuristics but could contain a Taylor series expansion. Therefore, the objectives of the present study are to present the unbiased estimates for variance following the transformation of the predicted variable and to identify an expansion of the Taylor series that does not induce numerical bias for mean and variance. We proved that the Taylor series expansion present in the unbiased expectation of mean and variance depends on the variance. We illustrated the new modeling approach on two problems, one at the ecosystem level, namely site productivity, and one at individual tree level, namely stem taper. The two models are unbiased, more parsimonious, and more precise than the existing less parsimonious models. This study focuses on research methods, which could be applied in similar studies of other species, ecosystem, as well as in behavioral sciences and econometrics.

Credit-Based Mobility Management Considering Travelers’ Budgeting Behaviors Under Uncertainty

This study analyzes the performance of a credit-based mobility management scheme considering travelers’ budgeting behaviors for credit consumption under uncertainty. In the scheme, government agencies periodically distribute a certain number of credits to travelers; travelers must pay a credit charge for driving to complete their trips. Otherwise, they can take public transit free of credit charge. Consequently, within a credit-releasing cycle, travelers must budget their credit consumption to fulfill their mobility needs. Such budgeting behaviors can be viewed as a multistage decision-making process under uncertainty. Considering a transportation system with a credit scheme, we propose parsimonious models to investigate how the uncertainty associated with individual mobility needs and the subsequent travelers’ credit-budgeting behavior influence the multistage equilibrium of the transportation system, as well as the performance of the credit scheme on managing the transportation system. Both analytical and numerical results suggest that travelers tend to restrict their credit consumption in the early stage of a credit-releasing cycle to hedge against the risks associated with using up all credits, which compromises the performances of credit-based schemes. Moreover, a negative attitude toward risk aggravates the discrepancy between the credit consumption of the early and late stages. Last, we propose a contingency credit scheme to mitigate the negative impact incurred by travelers’ budgeting behaviors.