Medical Expenditure Puzzle

What does your medical expenditure do to your health? Researchers often get significant negative sign on the relative coefficient in the reduced form health production regression. The puzzling result motivates this simple dynamic quantitative general equilibrium model to study the relationships between health status, medical expenditure and employment. The structural parameters are estimated by an indirect inference procedure. This paper finds that the simulated coefficient of medical expenditure in the health equation is negative even though in the health evolution equation of the structural model, medical expenditure only impacts the health in the positive waymedical expenditure, employment, dynamics, indirect inference

A Spiritual Ecological Study of Toni Morrison’s Paradise

As one of the first African American women that won the Nobel Prize for literature in 1993, Toni Morrison(1931-2019) is one of the most influential writers in the contemporary American literary world. Through a comparative analysis of Ruby residents and Covent women, this thesis explores how the Covent women overcome the spiritual crisis as well as the root cause for the decline in Ruby, and excavates Morrison’s ecological ideas embodied in Paradise. It also reflects Morrison’s initial exploration of the establishment of a harmonious society for black people and her expectations that the black community can achieve self-identification and finally get rid of the trauma

DISTINGUISHING CONTINUOUS AND DISCRETE APPROACHES TO MULTILEVEL MIXTURE IRT MODELS: A MODEL COMPARISON PERSPECTIVE

The current study introduced a general modeling framework, multilevel mixture IRT (MMIRT) which detects and describes characteristics of population heterogeneity, while accommodating the hierarchical data structure. In addition to introducing both continuous and discrete approaches to MMIRT, the main focus of the current study was to distinguish continuous and discrete MMIRT models from a model comparison perspective. A simulation study was conducted to evaluate the impact of class separation, cluster size, proportion of mixture, and between-group ability variance on the model performance of a set of MMIRT models. The behavior of information-based fit criteria in distinguishing between discrete and continuous MMIRT models was also investigated. An empirical analysis was presented to illustrate the application of MMIRT models. Results suggested that class separation, and between-group ability variance had significant impact on MMIRT model performance. Discrete MMIRT models with fewer group-level latent classes performed consistently better on parameter and classification recovery than the continuous MMIRT model and the discrete models with more latent classes at the group level. Despite the poor performance of the continuous MMIRT model, it was favored over the discrete models by most fit indices. The AIC, AIC3, AICC, and the modification of AIC and ssBIC were more sensitive to the discreteness in random effect distribution, compared to the CAIC, BIC, their modifications, and ssBIC. The latter ones had a higher tendency to select continuous MMIRT model as the best fitting model, regardless of the true distribution of random effects

Extracting Information from Compact Binary Coalescences with Gravitational Waves

Gravitational waves (GWs) radiated by compact binary coalescences (CBCs) carry useful information about their sources. These source properties obtained via the parameter estimation technique can help us to answer a wide range of physics problems. In this dissertation, I will present three major research projects. Firstly, binary neutron stars (BNSs) detected by Advanced LIGO and Advanced Virgo are ideal to study the equation of state (EoS). The EoS enters GW waveforms through tidal deformability, which can be measured by Advanced LIGO and Advanced Virgo. By performing Bayesian model selection, we can test plausible models from a large set of proposed EoSs. Secondly, the time delay between GW detectors can be used to measure the speed of gravitational waves. Although the uncertainty of results produced by this method is larger than using the time delay between GW and gamma-ray burst (GRB), it does provide a model independent means to measure the speed of gravitational waves. Finally, gravitational waves that lensed by galaxies or galaxy clusters are expected to produce multiple images with the time delay ranging from minutes to months. The fact that lensed GW signals are sharing some source properties allows us to identify potential lensed GW events by comparing Bayesian evidences between individual parameter estimation runs and joint parameter estimation runs

Palladium and silver abundances in stars with [Fe/H] > -2.6

Palladium (Pd) and silver (Ag) are the key elements for probing the weak component in the rapid neutron-capture process (r-process) of stellar nucleosynthesis. We performed a detailed analysis of the high-resolution and high signal-to-noise ratio near-UV spectra from the archive of HIRES on the Keck telescope, UVES on the VLT, and HDS on the Subaru Telescope, to determine the Pd and Ag abundances of 95 stars. This sample covers a wide metallicity range with -2.6 $\lesssim$ [Fe/H] $\lesssim$ +0.1, and most of them are dwarfs. The plane-parallel LTE MAFAGS-OS model atmosphere was adopted, and the spectral synthesis method was used to derive the Pd and Ag abundances from Pd I {\lambda} 3404 {\AA} and Ag I {\lambda} 3280/3382 {\AA} lines. We found that both elements are enhanced in metal-poor stars, and their ratios to iron show flat trends at -0.6 < [Fe/H] < +0.1. The abundance ratios of [Ag/H] and [Pd/H] are well correlated over the whole abundance range. This implies that Pd and Ag have similar formation mechanisms during the Galactic evolution.Comment: 15 pages, 12 figures, accepted to A&

Empirical modeling for intelligent, real-time manufacture control

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper

Geometry of holomorphic invariant strongly pseudoconvex complex Finsler metrics on the classical domains

In this paper, a class of holomorphic invariant metrics are introduced on the irreducible classical domains of type I-IV , which by their very definitions are strongly pseudoconvex complex Finsler metrics in the strict sense of M. Abate and G. Patrizio [2]. These metrics are of particular interesting since they are holomorphic invariant non-Hermitian quadratic complex Finsler metrics found so far in literature which enjoy good regularity as well as convexity and can be explicitly expressed so as to admit differential geometric studies. These metrics are explicitly constructed via deformations of the corresponding Bergman metrics on the irreducible classical domains and they are all proved to be complete K\"ahler-Berwald metrics. Moreover, these metrics enjoy very similar curvature properties as that of the Bergman metrics on the irreducible classical domains, namely they all have negative holomorphic sectional curvatures and non-positive holomorphic bisectional curvatures. From the view point of complex analysis, these metrics are analogue of Bergman metrics in complex Finsler geometry without Hermitian quadratic restrictions in the philosophy of S. S. Chern [8]; furthermore, they are all K\"ahler Finsler-Einstein metrics in the sense of T. Aikou [5]