A data driven equivariant approach to constrained Gaussian mixture modeling

Maximum likelihood estimation of Gaussian mixture models with different class-specific covariance matrices is known to be problematic. This is due to the unboundedness of the likelihood, together with the presence of spurious maximizers. Existing methods to bypass this obstacle are based on the fact that unboundedness is avoided if the eigenvalues of the covariance matrices are bounded away from zero. This can be done imposing some constraints on the covariance matrices, i.e. by incorporating a priori information on the covariance structure of the mixture components. The present work introduces a constrained equivariant approach, where the class conditional covariance matrices are shrunk towards a pre-specified matrix Psi. Data-driven choices of the matrix Psi, when a priori information is not available, and the optimal amount of shrinkage are investigated. The effectiveness of the proposal is evaluated on the basis of a simulation study and an empirical example

Principal Stratification in Sample Selection Problems with Non Normal Error Terms

The aim of the paper is to relax distributional assumptions on the error terms, often imposed in parametric sample selection models to estimate causal effects, when plausible exclusion restrictions are not available. Within the principal stratification framework, we approximate the true distribution of the error terms with a mixture of Gaussian. We propose an EM type algorithm for ML estimation. In a simulation study we show that our estimator has lower MSE than the ML and two-step Heckman estimators with any non normal distribution considered for the error terms. Finally we provide an application to the Job Corps training program

Tests of SNIS Josephson Arrays Cryocooler Operation

Cryogen-free operation of is essential to spread applications of superconductivity and is indeed unavoidable in some cases. In electrical metrology applications, higher temperature operation to reduce the refrigerator size and complexity is not yet possible, since arrays of Josephson junctions for voltage standard applications made with high-temperature superconductors are not yet available. The superconductor-normal metal-insulator-superconductor (SNIS) technology developed at INRIM uses low temperature superconductors, but allows operation well above liquid helium temperature. It is thus interesting for application to a compact cryocooled standard. We studied SNIS devices cooled with a closed-cycle refrigerator, both in DC and under RF irradiation. Issues related to thermal design of the apparatus are analyzed. The dependence of RF steps on the number of junctions observed is discussed in detail and interpreted as a consequence of power dissipated inside the chip

Role of a clinical prediction score in a chronic thromboembolic pulmonary hypertension rule-out strategy

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Comparison of seven prognostic tools to identify low-risk pulmonary embolism in patients aged <50 years

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Association Between Preexisting Versus Newly Identified Atrial Fibrillation and Outcomes of Patients With Acute Pulmonary Embolism

Background Atrial fibrillation (AF) may exist before or occur early in the course of pulmonary embolism (PE). We determined the PE outcomes based on the presence and timing of AF. Methods and Results Using the data from a multicenter PE registry, we identified 3 groups: (1) those with preexisting AF, (2) patients with new AF within 2 days from acute PE (incident AF), and (3) patients without AF. We assessed the 90-day and 1-year risk of mortality and stroke in patients with AF, compared with those without AF (reference group). Among 16 497 patients with PE, 792 had preexisting AF. These patients had increased odds of 90-day all-cause (odds ratio [OR], 2.81; 95% CI, 2.33-3.38) and PE-related mortality (OR, 2.38; 95% CI, 1.37-4.14) and increased 1-year hazard for ischemic stroke (hazard ratio, 5.48; 95% CI, 3.10-9.69) compared with those without AF. After multivariable adjustment, preexisting AF was associated with significantly increased odds of all-cause mortality (OR, 1.91; 95% CI, 1.57-2.32) but not PE-related mortality (OR, 1.50; 95% CI, 0.85-2.66). Among 16 497 patients with PE, 445 developed new incident AF within 2 days of acute PE. Incident AF was associated with increased odds of 90-day all-cause (OR, 2.28; 95% CI, 1.75-2.97) and PE-related (OR, 3.64; 95% CI, 2.01-6.59) mortality but not stroke. Findings were similar in multivariable analyses. Conclusions In patients with acute symptomatic PE, both preexisting AF and incident AF predict adverse clinical outcomes. The type of adverse outcomes may differ depending on the timing of AF onset.info:eu-repo/semantics/publishedVersio

Three-mode component analysis with crisp or fuzzy partition of units

A new methodology is proposed for the simultaneous reduction of units, variables, and occasions of a three-mode data set. Units are partitioned into a reduced number of classes, while, simultaneously, components for variables and occasions accounting for the largest common information for the classification are identified. The model is a constrained three-mode factor analysis and it can be seen as a generalization of the REDKM model proposed by De Soete and Carroll for two-mode data. The least squares fitting problem is mathematically formalized as a constrained problem in continuous and discrete variables. An iterative alternating least squares algorithm is proposed to give an efficient solution to this minimization problem in the crisp and fuzzy classification context. The performances of the proposed methodology are investigated by a simulation study comparing our model with other competing methodologies. Different procedures for starting the proposed algorithm have also been tested. A discussion of some interesting differences in the results follows. Finally, an application to real data illustrates the ability of the proposed model to provide substantive insights into the data complexities