126 research outputs found
Essays on panel data and sample selection methods
The availability of panel data allows researchers to control for unobserved heterogeneity in economic models, but raises important computational and statistical challenges. For instance, fixed effects estimators suffer from the incidental parameter problem and lead to high-dimensional estimation problems. In this dissertation, I aim to address both theoretical and practical issues in the estimation of panel data models.
Sample selection is one of the most common forms of endogeneity in empirical economics. It arises when the main dependent variable is selected into the sample through a nonrandom process. The classical solution to account for sample selection is the Heckman selection model (HSM). In this dissertation, I extend the HSM in two dimensions: (1) I relax the homogeneity restrictions that the HSM imposes; and (2) I develop a panel data version of the model that accounts for unobserved heterogeneity.
In Chapter 1, I develop a distribution regression model with sample selection for panel and network data. The model is a semiparametric generalization of the HSM that accommodates much richer patterns of heterogeneity in the selection process, covariates and unobserved effects. I provide a computationally attractive two-step fixed-effect estimation procedure, a bias correction method and a multiplier bootstrap algorithm to conduct uniform inference on the function-valued model parameters. I apply this model to the gravity equation of international trade network accounting for possibly endogenous zero trade decisions and unobserved country heterogeneity.
Chapter 2 focuses on the distribution regression model with sample selection for cross-sectional data. In this chapter, I study the identification of the model and apply the model to wage decompositions in the UK accounting for possibly endogeneous selection into employment. Here I decompose the difference between the male and female wage distributions into four effects: composition, wage structure, selection structure and selection sorting.
In Chapter 3, I propose a novel estimation algorithm for panel data models with multiple high-dimensional fixed effects and missing data. The algorithm absorbs the fixed effects iteratively until they are eventually eliminated. Applying this algorithm to a large-scale US employer-based health insurance data, I conclude that narrow network plans reduce health care utilization
Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK
We develop a distribution regression model under endogenous sample selection.
This model is a semiparametric generalization of the Heckman selection model
that accommodates much richer patterns of heterogeneity in the selection
process and effect of the covariates. The model applies to continuous, discrete
and mixed outcomes. We study the identification of the model, and develop a
computationally attractive two-step method to estimate the model parameters,
where the first step is a probit regression for the selection equation and the
second step consists of multiple distribution regressions with selection
corrections for the outcome equation. We construct estimators of functionals of
interest such as actual and counterfactual distributions of latent and observed
outcomes via plug-in rule. We derive functional central limit theorems for all
the estimators and show the validity of multiplier bootstrap to carry out
functional inference. We apply the methods to wage decompositions in the UK
using new data. Here we decompose the difference between the male and female
wage distributions into four effects: composition, wage structure, selection
structure and selection sorting. After controlling for endogenous employment
selection, we still find substantial gender wage gap -- ranging from 21% to 40%
throughout the (latent) offered wage distribution that is not explained by
observable labor market characteristics. We also uncover positive sorting for
single men and negative sorting for married women that accounts for a
substantive fraction of the gender wage gap at the top of the distribution.
These findings can be interpreted as evidence of assortative matching in the
marriage market and glass-ceiling in the labor market.Comment: 72 pages, 4 tables, 39 figures, includes supplement with additional
empirical result
Influence of the Feed Moisture, Rotor Speed, and Blades Gap on the Performances of a Biomass Pulverization Technology
Recently, a novel biomass pulverization technology was proposed by our group. In this paper, further detailed studies of this technology were carried out. The effects of feed moisture and crusher operational parameters (rotor speed and blades gap) on product particle size distribution and energy consumption were investigated. The results showed that higher rotor speed and smaller blades gap could improve the hit probability between blades and materials and enhance the impacting and grinding effects to generate finer products, however, resulting in the increase of energy consumption. Under dry conditions finer particles were much more easily achieved, and there was a tendency for the specific energy to increase with increasing feed moisture. Therefore, it is necessary for the raw biomass material to be dried before pulverization
One symbol blind synchronization in SIMO molecular communication systems
Molecular communication offers new possibilities in the micro-and nano-scale application environments. Similar to other communication paradigms, molecular communication also requires clock synchronization between the transmitter and the receiver nanomachine in many time-and control-sensitive applications. This letter presents a novel high-efficiency blind clock synchronization mechanism. Without knowing the channel parameters of the diffusion coefficient and the transmitter-receiver distance, the receiver only requires one symbol to achieve synchronization. The samples are used to estimate the propagation delay by least square method and achieve clock synchronization. Single-input multiple-output (SIMO) diversity design is then proposed to mitigate channel noise and therefore to improve the synchronization accuracy. The simulation results show that the proposed clock synchronization mechanism has a good performance and may help chronopharmaceutical drug delivery applications
SAR Ship Target Recognition via Selective Feature Discrimination and Multifeature Center Classifier
Maritime surveillance is not only necessary for every country, such as in
maritime safeguarding and fishing controls, but also plays an essential role in
international fields, such as in rescue support and illegal immigration
control. Most of the existing automatic target recognition (ATR) methods
directly send the extracted whole features of SAR ships into one classifier.
The classifiers of most methods only assign one feature center to each class.
However, the characteristics of SAR ship images, large inner-class variance,
and small interclass difference lead to the whole features containing useless
partial features and a single feature center for each class in the classifier
failing with large inner-class variance. We proposes a SAR ship target
recognition method via selective feature discrimination and multifeature center
classifier. The selective feature discrimination automatically finds the
similar partial features from the most similar interclass image pairs and the
dissimilar partial features from the most dissimilar inner-class image pairs.
It then provides a loss to enhance these partial features with more interclass
separability. Motivated by divide and conquer, the multifeature center
classifier assigns multiple learnable feature centers for each ship class. In
this way, the multifeature centers divide the large inner-class variance into
several smaller variances and conquered by combining all feature centers of one
ship class. Finally, the probability distribution over all feature centers is
considered comprehensively to achieve an accurate recognition of SAR ship
images. The ablation experiments and experimental results on OpenSARShip and
FUSAR-Ship datasets show that our method has achieved superior recognition
performance under decreasing training SAR ship samples
Crucial Feature Capture and Discrimination for Limited Training Data SAR ATR
Although deep learning-based methods have achieved excellent performance on
SAR ATR, the fact that it is difficult to acquire and label a lot of SAR images
makes these methods, which originally performed well, perform weakly. This may
be because most of them consider the whole target images as input, but the
researches find that, under limited training data, the deep learning model
can't capture discriminative image regions in the whole images, rather focus on
more useless even harmful image regions for recognition. Therefore, the results
are not satisfactory. In this paper, we design a SAR ATR framework under
limited training samples, which mainly consists of two branches and two
modules, global assisted branch and local enhanced branch, feature capture
module and feature discrimination module. In every training process, the global
assisted branch first finishes the initial recognition based on the whole
image. Based on the initial recognition results, the feature capture module
automatically searches and locks the crucial image regions for correct
recognition, which we named as the golden key of image. Then the local extract
the local features from the captured crucial image regions. Finally, the
overall features and local features are input into the classifier and
dynamically weighted using the learnable voting parameters to collaboratively
complete the final recognition under limited training samples. The model
soundness experiments demonstrate the effectiveness of our method through the
improvement of feature distribution and recognition probability. The
experimental results and comparisons on MSTAR and OPENSAR show that our method
has achieved superior recognition performance
SAR ATR Method with Limited Training Data via an Embedded Feature Augmenter and Dynamic Hierarchical-Feature Refiner
Without sufficient data, the quantity of information available for supervised
training is constrained, as obtaining sufficient synthetic aperture radar (SAR)
training data in practice is frequently challenging. Therefore, current SAR
automatic target recognition (ATR) algorithms perform poorly with limited
training data availability, resulting in a critical need to increase SAR ATR
performance. In this study, a new method to improve SAR ATR when training data
are limited is proposed. First, an embedded feature augmenter is designed to
enhance the extracted virtual features located far away from the class center.
Based on the relative distribution of the features, the algorithm pulls the
corresponding virtual features with different strengths toward the
corresponding class center. The designed augmenter increases the amount of
information available for supervised training and improves the separability of
the extracted features. Second, a dynamic hierarchical-feature refiner is
proposed to capture the discriminative local features of the samples. Through
dynamically generated kernels, the proposed refiner integrates the
discriminative local features of different dimensions into the global features,
further enhancing the inner-class compactness and inter-class separability of
the extracted features. The proposed method not only increases the amount of
information available for supervised training but also extracts the
discriminative features from the samples, resulting in superior ATR performance
in problems with limited SAR training data. Experimental results on the moving
and stationary target acquisition and recognition (MSTAR), OpenSARShip, and
FUSAR-Ship benchmark datasets demonstrate the robustness and outstanding ATR
performance of the proposed method in response to limited SAR training data
- …