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Articles 1 - 11 of 11
Full-Text Articles in Statistical Models
Effects Of Functional Network Model Definition On Biomarker Outcome Prediction, Xinyang Feng
Effects Of Functional Network Model Definition On Biomarker Outcome Prediction, Xinyang Feng
Arts & Sciences Electronic Theses and Dissertations
Machine learning (ML) models are widely used to investigate the human connectome and to predict and understand behavior, emotion, and cognition. Prior research has organized pediatric connectome data using adult functional network models. However, this assumes that adult functional network models are appropriate and useful for prediction developmental outcomes from pediatric connectome data. We hypothesize that the application of adult brain network models could result in poor model fit, limiting the generalizability of results. Here, we test whether prediction of biological age is improved by concordant brain network models matching underlying functional connectome data. To quantify the difference in age …
Adaptive Optimal Market Making Strategies With Inventory Liquidation Cost, Yi Zhang
Adaptive Optimal Market Making Strategies With Inventory Liquidation Cost, Yi Zhang
Arts & Sciences Electronic Theses and Dissertations
Along the lines of the paper \cite{zoe}, we find a general form of the optimal market making strategy for a high-frequency market maker (HFM) in a discrete-time Limit Order Book (LOB) model. Unlike \cite{zoe}, the optimal market making strategy is adaptive depending on the arrival of Market Order (MO) in the previous time intervals. We provide a method to make each placement of Limit Orders (LO) dependent on previous information in the same trading day and prove the admissibility of the optimal market making strategy under some general assumptions. Empirical study shows the adaptive optimal strategies outperform the non-adaptive strategy …
Deep Learning Analysis Of Limit Order Book, Xin Xu
Deep Learning Analysis Of Limit Order Book, Xin Xu
Arts & Sciences Electronic Theses and Dissertations
In this paper, we build a deep neural network for modeling spatial structure in limit order book and make prediction for future best ask or best bid price based on ideas of (Sirignano 2016). We propose an intuitive data processing method to approximate the data is non-available for us based only on level I data that is more widely available. The model is based on the idea that there is local dependence for best ask or best bid price and sizes of related orders. First we use logistic regression to prove that this approach is reasonable. To show the advantages …
Variable Selection Via Lasso With High-Dimensional Proteomic Data, Hongxuan Zhai
Variable Selection Via Lasso With High-Dimensional Proteomic Data, Hongxuan Zhai
Arts & Sciences Electronic Theses and Dissertations
Multiclass classification with high-dimensional data is an applied topic both in statistics and machine learning. The classification procedure could be done in various ways. In this thesis, we review the theory of the Lasso procedure which provides a parameter estimator while simultaneously achieving dimension reduction due to a property of the L1 norm. Lasso with elastic net penalty and sparse group lasso are also reviewed. Our data is high-dimensional proteomic data (iTRAQ ratios) of breast cancer patients with four subtypes of breast cancer. We use the multinomial logistic regression to train our classifier and use the false classification rates obtained …
Algorithmic Trading With Prior Information, Xinyi Cai
Algorithmic Trading With Prior Information, Xinyi Cai
Arts & Sciences Electronic Theses and Dissertations
Traders utilize strategies by using a mix of market and limit orders to generate profits. There are different types of traders in the market, some have prior information and can learn from changes in prices to tweak her trading strategy continuously(Informed Traders), some have no prior information but can learn(Uninformed Learners), and some have no prior information and cannot learn(Uninformed Traders). In this thesis. Alvaro C, Sebastian J and Damir K \cite{AL} proposed a model for algorithmic traders to access the impact of dynamic learning in profit and loss in 2014. The traders can employ the model to decide which …
Nonparametric Estimation Of Time Series Volatility Model Estimation, Teng Tu
Nonparametric Estimation Of Time Series Volatility Model Estimation, Teng Tu
Arts & Sciences Electronic Theses and Dissertations
In this article we consider two estimation methods of a non-parametric volatility model with autoregressive error of order two. The first estimation method based on the two- lag difference. To get a better result, we consider the second approach based on the general quadratic forms. For illustration, we provided several data sets from different simulation models to support the procedures of both two methods, and prove that the second approach can make a better estimation.
Mortgage Transition Model Based On Loanperformance Data, Shuyao Yang
Mortgage Transition Model Based On Loanperformance Data, Shuyao Yang
Arts & Sciences Electronic Theses and Dissertations
The unexpected increase in loan default on the mortgage market is widely considered to be one of the main cause behind the economic crisis. To provide some insight on loan delinquency and default, I analyze the mortgage performance data from Fannie Mae website and investigate how economic factors and individual loan and borrower information affect the events of default and prepaid. Various delinquency status including default and prepaid are treated as discrete states of a Markov chain. One-step transition probabilities are estimated via multinomial logistic models. We find that in general current loan-to-value ratio, credit score, unemployment rate, and interest …
On Post-Selection Confidence Intervals In Linear Regression, Xinwei Zhang
On Post-Selection Confidence Intervals In Linear Regression, Xinwei Zhang
Arts & Sciences Electronic Theses and Dissertations
The general goal of this thesis is to investigate and examine some issues about post-selection inference which arises from the setting where statistical inference is carried out after a datadriven model selection step. In this setting, the classical inference theory which requires a fixed priori model becomes invalid since the selected model is a result of random event. Hence, a common practice in applied research which ignores the model selection and builds up confidence interval will result in misleading or even false conclusion. In this thesis, specifically, we first discusses some examples to show how the classical inference theory loses …
Statistical Analysis Of Markovian Queueing Models Of Limit Order Books, Yiyao Luo
Statistical Analysis Of Markovian Queueing Models Of Limit Order Books, Yiyao Luo
Arts & Sciences Electronic Theses and Dissertations
The objective of this thesis is to investigate the suitability of some Markovian queueing models in being able to effectively describe the dynamical properties of a limit order book more specifically. We review and compare the assumptions proposed by Huang et al.[Quantitative Finance,12,547-557(2012)] and Cont et al.[SIAM Journal for Financial Mathematics,4,1- 25(2013)], and estimate the intensity parameters in both ways, based on real data of a stock on the Nasdaq Stock Market. Trough comparing by cumulative distribution functions of first-passage time to state 0, we will hsow that the estimators of Cont’s model fit our data better and we put …
Examining Cost Functionality And Optimization: A Case Study On Testing The Reasonableness Of New Aircraft Using Historical Aircraft Data, Katherine Jozefiak
Examining Cost Functionality And Optimization: A Case Study On Testing The Reasonableness Of New Aircraft Using Historical Aircraft Data, Katherine Jozefiak
Arts & Sciences Electronic Theses and Dissertations
When pursuing business by competing for government contracts, proving the submitted price is reasonable is often required. This proof is called a test of reasonableness. This study analyzes data from historical aircraft programs in relation of a new aircraft program in order to demonstrate the estimated cost of the new program is reasonable. The purpose of this study is to investigate three questions. Is the new program cost reasonable using current industry and government parameters? Is it better to look at programs from a total cost perspective or break the total cost into subcategory levels? Finally, this study applies a …
Spot Volatility Estimation Of Ito Semimartingales Using Delta Sequences, Weixuan Gao
Spot Volatility Estimation Of Ito Semimartingales Using Delta Sequences, Weixuan Gao
Arts & Sciences Electronic Theses and Dissertations
This thesis studies a unifying class of nonparametric spot volatility estimators proposed by Mancini et. al.(2013). This method is based on delta sequences and is conceived to include many of the existing estimators in the field as special cases. The thesis first surveys the asymptotic theory of the proposed estimators under an infill asymptotic scheme and fixed time horizon, when the state variable follows a Brownian semimartingale. Then, some extensions to include jumps and financial microstructure noise in the observed price process are also presented. The main goal of the thesis is to assess the suitability of the proposed methods …