Nonparametric Estimation and Inference in Econometrics

This dissertation includes two essays: The first one is on nonparametric inference in causal effect models, and the second one is on nonparametric estimation in financial economics. In the first essay, we propose a nonparametric test for unobserved heterogeneous treatment effects in a general framework, allowing for self-selection to the treatment. The proposed modified Kolmogorov-Smirnov-type test is consistent and simple to implement. Monte Carlo simulations show that our test performs well in finite samples. For illustration, we apply our test to study heterogeneous treatment effects of the Job Training Partnership Act on earnings and the impacts of fertility on family income. In the second essay, we provide an alternative to the existing estimations of implied volatility in option pricing. The use of state price densities to gather information about market sentiment and other empirical characteristics that describe important phenomena is popular in literature and in practice. The estimation of the implied volatility surface to extract these densities is a crucial intermediate step in the process, and the methods to do so are varied in literature. This essay proposes an estimation procedure that is relative new in nonparametric literature: `1 trend filtering. We show its advantages over typically used nonparametric and parametric methods, commonly used in literature and in practice, to deal with this particular estimation problem. Additionally, the method maintains smaller prediction errors than the comparison models across different number of observations and levels of noise

Nonparametric Estimation and Inference in Econometrics

This dissertation includes two essays: The first one is on nonparametric inference in causal effect models, and the second one is on nonparametric estimation in financial economics. In the first essay, we propose a nonparametric test for unobserved heterogeneous treatment effects in a general framework, allowing for self-selection to the treatment. The proposed modified Kolmogorov-Smirnov-type test is consistent and simple to implement. Monte Carlo simulations show that our test performs well in finite samples. For illustration, we apply our test to study heterogeneous treatment effects of the Job Training Partnership Act on earnings and the impacts of fertility on family income. In the second essay, we provide an alternative to the existing estimations of implied volatility in option pricing. The use of state price densities to gather information about market sentiment and other empirical characteristics that describe important phenomena is popular in literature and in practice. The estimation of the implied volatility surface to extract these densities is a crucial intermediate step in the process, and the methods to do so are varied in literature. This essay proposes an estimation procedure that is relative new in nonparametric literature: `1 trend filtering. We show its advantages over typically used nonparametric and parametric methods, commonly used in literature and in practice, to deal with this particular estimation problem. Additionally, the method maintains smaller prediction errors than the comparison models across different number of observations and levels of noise

Regulation of axon repulsion by MAX-1 SUMOylation and AP-3.

During neural development, growing axons express specific surface receptors in response to various environmental guidance cues. These axon guidance receptors are regulated through intracellular trafficking and degradation to enable navigating axons to reach their targets. In Caenorhabditis elegans, the UNC-5 receptor is necessary for dorsal migration of developing motor axons. We previously found that MAX-1 is required for UNC-5-mediated axon repulsion, but its mechanism of action remained unclear. Here, we demonstrate that UNC-5-mediated axon repulsion in C. elegans motor axons requires both max-1 SUMOylation and the AP-3 complex β subunit gene, apb-3 Genetic interaction studies show that max-1 is SUMOylated by gei-17/PIAS1 and acts upstream of apb-3 Biochemical analysis suggests that constitutive interaction of MAX-1 and UNC-5 receptor is weakened by MAX-1 SUMOylation and by the presence of APB-3, a competitive interactor with UNC-5. Overexpression of APB-3 reroutes the trafficking of UNC-5 receptor into the lysosome for protein degradation. In vivo fluorescence recovery after photobleaching experiments shows that MAX-1 SUMOylation and APB-3 are required for proper trafficking of UNC-5 receptor in the axon. Our results demonstrate that SUMOylation of MAX-1 plays an important role in regulating AP-3-mediated trafficking and degradation of UNC-5 receptors during axon guidance

Image operator learning coupled with CNN classification and its application to staff line removal

Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue when using learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solution outperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site.Comment: To appear in ICDAR 201

Inhibition of yes-associated protein suppresses brain metastasis of human lung adenocarcinoma in a murine model.

Yes-associated protein (YAP) is a main mediator of the Hippo pathway and promotes cancer development and progression in human lung cancer. We sought to determine whether inhibition of YAP suppresses metastasis of human lung adenocarcinoma in a murine model. We found that metastatic NSCLC cell lines H2030-BrM3(K-rasG12C mutation) and PC9-BrM3 (EGFRΔexon19 mutation) had a significantly decreased p-YAP(S127)/YAP ratio compared to parental H2030 (K-rasG12C mutation) and PC9 (EGFRΔexon19 mutation) cells (P < .05). H2030-BrM3 cells had significantly increased YAP mRNA and expression of Hippo downstream genes CTGF and CYR61 compared to parental H2030 cells (P < .05). Inhibition of YAP by short hairpin RNA (shRNA) and small interfering RNA (siRNA) significantly decreased mRNA expression in downstream genes CTGF and CYR61 in H2030-BrM3 cells (P < .05). In addition, inhibiting YAP by YAP shRNA significantly decreased migration and invasion abilities of H2030-BrM3 cells (P < .05). We are first to show that mice inoculated with YAP shRNA-transfected H2030-BrM3 cells had significantly decreased metastatic tumour burden and survived longer than control mice (P < .05). Collectively, our results suggest that YAP plays an important role in promoting lung adenocarcinoma brain metastasis and that direct inhibition of YAP by shRNA suppresses H2030-BrM3 cell brain metastasis in a murine model

Secondary Metabolites from the Leaves of Aquilaria agallocha

Twelve compounds, including three flavonoids, 5-hydroxy-4¢,7- dimethoxyflavone (1) [22], luteolin-7,3¢,4¢-trimethyl ether (2) and 5,3¢- dihydroxy-7,4¢-dimethoxyflavone (3), five benzenoids, methylparaben (4), vanillic acid (5), p-hydroxybenzoic acid (6), syringic acid (7), and isovanillic acid (8) and four steroids, b-sitosterol (9), stigmasterol (10), b-sitostenone (11) and stigmasta-4,22-dien-3- one (12) were isolated from the leaves of Aquilaria agallocha (Thymelaeaceae). All of these compounds (1-12) were obtained for the first time from the leaves of this plant

Ventricular divergence correlates with epicardial wavebreaks and predicts ventricular arrhythmia in isolated rabbit hearts during therapeutic hypothermia

INTRODUCTION: High beat-to-beat morphological variation (divergence) on the ventricular electrogram during programmed ventricular stimulation (PVS) is associated with increased risk of ventricular fibrillation (VF), with unclear mechanisms. We hypothesized that ventricular divergence is associated with epicardial wavebreaks during PVS, and that it predicts VF occurrence. METHOD AND RESULTS: Langendorff-perfused rabbit hearts (n = 10) underwent 30-min therapeutic hypothermia (TH, 30°C), followed by a 20-min treatment with rotigaptide (300 nM), a gap junction modifier. VF inducibility was tested using burst ventricular pacing at the shortest pacing cycle length achieving 1:1 ventricular capture. Pseudo-ECG (p-ECG) and epicardial activation maps were simultaneously recorded for divergence and wavebreaks analysis, respectively. A total of 112 optical and p-ECG recordings (62 at TH, 50 at TH treated with rotigaptide) were analyzed. Adding rotigaptide reduced ventricular divergence, from 0.13±0.10 at TH to 0.09±0.07 (p = 0.018). Similarly, rotigaptide reduced the number of epicardial wavebreaks, from 0.59±0.73 at TH to 0.30±0.49 (p = 0.036). VF inducibility decreased, from 48±31% at TH to 22±32% after rotigaptide infusion (p = 0.032). Linear regression models showed that ventricular divergence correlated with epicardial wavebreaks during TH (p<0.001). CONCLUSION: Ventricular divergence correlated with, and might be predictive of epicardial wavebreaks during PVS at TH. Rotigaptide decreased both the ventricular divergence and epicardial wavebreaks, and reduced the probability of pacing-induced VF during TH