Semi-parametric Adjustment Model Methods for Positioning of Seafloor Control Point

This paper focus on solving the problem of seafloor control point absolute positioning with low vertical accuracy based on the survey ship sailing circle. The method of dealing with the systematic error based on a semi-parametric adjustment model was proposed. Firstly, the influence of sound velocity change on ranging error is analyzed. Secondly, a semi-parametric adjustment model for determining three-dimensional coordinates of seafloor control points was established. And respectively proposed solutions under two different conditions, the observation duration is an integral multiple or non-integer multiple of the long-period term of the ranging error. The simulation experiment shows that this method can obviously improve the accuracy of vertical solution of seafloor control point compared with the difference technique and the least-squares method when internal waves exist and observation duration is less than an integer multiple of the long-period term of the ranging error.

Double Penalized Semi-Parametric Signed-Rank Regression with Adaptive LASSO

In this paper, a semi-parametric regression model with an adaptive LASSO penalty imposed on both the linear and the nonlinear components of the mode is considered. The model is rewritten so that a signed-rank technique can be used for estimation. The nonlinear part consists of a covariate that enters the model nonlinearly via an unknown function that is estimated using Bsplines. The author shows that the resulting estimator is consistent under heavy-tailed distributions and asymptotic normality results are given. Monte Carlo simulations as well as practical applications are studied to assess the validity of the proposed estimation method.

Asymptotic Properties of a Modified Semi-Parametric MLE in Linear Regression with Right-Censored Data

Consider a linear regression model Y=β'X+e.where Y may be right censored and the cdf F+o of e is unknown.We show that a modified semi-parametric MLE.denoted by .is strongly consistent under certain regularity conditions.Moreover.if F_o is discontinuous,then P(≠βi.o.)=0, which means that P(=βif the sample size is large)=1.The latter property has not been reported for the existing estimators.By contrast,most estimators,such as the Buckley-James estimator and M-estimators .satisfy that P(≠βi.o.)=1.

Separation of systematic errors in processing high precision GPS baselines

The research is based on the double difference observations and semi-parametric model. Systematic errors are considered as the parameters to be estimated, and brought into the GPS observation equations. High precision baselines are obtained after separating systematic errors. The crucial steps are choosing regularizer and regularization parameters in processing GPS systematic errors by using the semi-parametric model. We propose a new regularizer and apply it to dealing with systematic errors. Also, we compare it with one proposed by other researchers. This comparison is done when all the regularization parameters equal to one. The computation result of the example shows that two regularizers correspond well and they can separate systematic errors successfully. Thus, we can get high precision baselines. Compared with R=QK-1Q′, our regularizer R=GTG is simple, so, the process of resolving the high precision baselines is relatively simple.

Strong Consistency of Estimators under Missing Responses

In this article, we focus on the semi-parametric error-in-variables model with missing responses: , where yi are the response variables missing at random, are design points, ζi are the potential variables observed with measurement errors μi, the unknown slope parameter ß?and nonparametric component g(·) need to be estimated. Here we choose two different approaches to estimate ß?and g(·). Under appropriate conditions, we study the strong consistency for the proposed estimators.

The Additive-multiplicative Hazards Model for Multiple Type of Recurrent Gap Times

Recurrent event gap times data frequently arise in biomedical studies and often more than one type of event is of interest. To evaluate the effects of covariates on the marginal recurrent event hazards functions, there exist two types of hazards models： the multiplicative hazards model and the additive hazards model. In the paper, we propose a more flexible additive-multiplicative hazards model for multiple type of recurrent gap times data, wherein some covariates are assumed to be additive while others are multiplicative. An estimating equation approach is presented to estimate the regression parameters. We establish asymptotic properties of the proposed estimators.

Marginal Distribution Plots for Proportional Hazards Models with Time-Dependent Covariates or Time-Varying Regression Coefficients

Given a sample of regression data from (Y, Z), a new diagnostic plotting method is proposed for checking the hypothesis H0: the data are from a given Cox model with the time-dependent covariates Z. It compares two estimates of the marginal distribution FY of Y. One is an estimate of the modified expression of FY under H0, based on a consistent estimate of the parameter under H0, and based on the baseline distribution of the data. The other is the Kaplan-Meier-estimator of FY, together with its confidence band. The new plot, called the marginal distribution plot, can be viewed as a test for testing H0. The main advantage of the test over the existing residual tests is in the case that the data do not satisfy any Cox model or the Cox model is mis-specified. Then the new test is still valid, but not the residual tests and the residual tests often make type II error with a very large probability.

The Statistical Analysis of Interval-Censored Failure Time Data with Applications

The analysis of survival data is a major focus of statistics. Interval censored data reflect uncertainty as to the exact times the units failed within an interval. This type of data frequently comes from tests or situations where the objects of interest are not constantly monitored. Thus events are known only to have occurred between the two observation periods. Interval censoring has become increasingly common in the areas that produce failure time data. This paper explores the statistical analysis of interval-censored failure time data with applications. Three different data sets, namely Breast Cancer, Hemophilia, and AIDS data were used to illustrate the methods during this study. Both parametric and nonparametric methods of analysis are carried out in this study. Theory and methodology of fitted models for the interval-censored data are described. Fitting of parametric and non-parametric models to three real data sets are considered. Results derived from different methods are presented and also compared.

Modelling and predicting low count child asthma hospital readmissions using General Additive Models

Background: Daily paediatric asthma readmissions within 28 days are a good example of a low count time series and not easily amenable to common time series methods used in studies of asthma seasonality and time trends. We sought to model and predict daily trends of childhood asthma readmissions over time inVictoria,Australia. Methods: We used a database of 75,000 childhood asthma admissions from the Department ofHealth,Victoria,Australiain 1997-2009. Daily admissions over time were modeled using a semi parametric Generalized Additive Model (GAM) and by sex and age group. Predictions were also estimated by using these models. Results: N = 2401 asthma readmissions within 28 days occurred during study period. Of these, n = 1358 (57%) were boys. Overall, seasonal peaks occurred in winter (30.5%) followed by autumn (28.6%) and then spring (24.6%) (p

The GWmodel R package:further topics for exploring spatial heterogeneity using geographically weighted models

In this study,we present a collection of local models,termed geographically weighted(GW)models,which can be found within the GWmodel R package.A GW model suits situations when spatial data are poorly described by the global form,and for some regions the localized fit provides a better description.The approach uses a moving window weighting technique,where a collection of local models are estimated at target locations.Commonly,model parameters or outputs are mapped so that the nature of spatial heterogeneity can be explored and assessed.In particular,we present case studies using:(i)GW summary statistics and a GW principal components analysis;(ii)advanced GW regression fits and diagnostics;(iii)associated Monte Carlo significance tests for non-stationarity;(iv)a GW discriminant analysis;and(v)enhanced kernel bandwidth selection procedures.General Election data-sets from the Republic of Ireland and US are used for demonstration.This study is designed to complement a companion GWmodel study,which focuses on basic and robust GW models.

Statistical regression modeling for energy consumption in wastewater treatment

Wastewater treatment is one of critical issues faced by water utilities, and receives more and more attentions recently. The energy consumption modeling in biochemical wastewater treatment was investigated in the study via a general and robust approach based on Bayesian semi-parametric quantile regression. The dataset was derived from a municipal wastewater treatment plant, where the energy consumption of unit chemical oxygen demand(COD) reduction was the response variable of interest. Via the proposed approach,the comprehensive regression pictures of the energy consumption and truly influencing factors, i.e., the regression relationships at lower, median and higher energy consumption levels were characterized respectively. Meanwhile, the proposals for energy saving in different cases were also facilitated specifically. First, the lower level of energy consumption was closely associated with the temperature of influent wastewater, and the chroma-rich wastewater also showed helpful in the execution of energy saving. Second, at median energy consumption level, the COD-rich wastewater played a determinative role in the reduction of energy consumption, while the higher quality of treated water led to slightly energy intensive. Third, the higher level of energy consumption was most likely to be attributed to the relatively high temperature of wastewater and total nitrogen(TN)-rich wastewater,and both of the factors were preferably to be avoided to alleviate the burden of energy consumption. The study provided an efficient approach to controlling the energy consumption of wastewater treatment in the perspective of statistical regression modeling, and offered valuable suggestions for the future energy saving.

Local asymptotic behavior of regression splines for marginal semiparametric models with longitudinal data

In this paper, we study the local asymptotic behavior of the regression spline estimator in the framework of marginal semiparametric model. Similarly to Zhu, Fung and He (2008), we give explicit expression for the asymptotic bias of regression spline estimator for nonparametric function f. Our results also show that the asymptotic bias of the regression spline estimator does not depend on the working covariance matrix, which distinguishes the regression splines from the smoothing splines and the seemingly unrelated kernel. To understand the local bias result of the regression spline estimator, we show that the regression spline estimator can be obtained iteratively by applying the standard weighted least squares regression spline estimator to pseudo-observations. At each iteration, the bias of the estimator is unchanged and only the variance is updated.

Analyzing Spatio-Temporal Distribution Pattern and Correlation for Taxi and Metro Ridership in Shanghai

Taxicab is an important mode in urban transportation system, while the role of taxicabs, especially the relationship with metro system has not been fully studied. This study aims at exploring the factors influencing the role played by taxicabs in Shanghai, China. Firstly, taxi trips are categorized into three types, namely metroreplaceable(MR), metro-extending(ME) and metro-supplement(MS) ones. Then, the tendency of travelers towards taxi or metro at a specific metro station is proposed and calculated on the basis of MR taxi trips and metro trips. Factors influencing the tendency are investigated through semi-parametric regression models, with the results indicating that the most significant factors and the influencing radii during the peak and off-peak hours are different. Some built environment factors, such as the number of hospitals and government agencies, have significant positive relationship with the tendency in the time periods. Furthermore, land use related factors, such as the increase of forestry and commercial land, generally promote taxi-hiring in the off-peak hours, while they have a negative impact during the peak hours. Findings of this study can assist governments and policy makers to understand the impact of built environment and land use on trip patterns, and thus may contribute to more reasonable policies and optimized urban planning, which may promote modal switch from taxi to subway.

Estimation of Partially Linear Panel Data Models with Cross-Sectional Dependence

This paper studies the estimation of the partially linear panel data models,allowing for cross-sectional dependence through a common factors structure.This semiparametric additive partial linear framework,including both linear and nonlinear additive components,is more flexible compared to linear models,and is preferred to a fully nonparametric regression because of the‘curse of dimensionality’.The consistency and asymptotic normality of the proposed estimators are established for the case where both cross-sectional dimension and temporal dimension go to infinity.The theoretical findings are further supported for small samples via a Monte Carlo study.The results suggest that the proposed method is robust to a wide variety of data generation processes.

Sufficient dimension reduction in the presence of controlling variables

We are concerned with partial dimension reduction for the conditional mean function in the presence of controlling variables.We suggest a profile least squares approach to perform partial dimension reduction for a general class of semi-parametric models.The asymptotic properties of the resulting estimates for the central partial mean subspace and the mean function are provided.In addition,a Wald-type test is proposed to evaluate a linear hypothesis of the central partial mean subspace,and a generalized likelihood ratio test is constructed to check whether the nonparametric mean function has a specific parametric form.These tests can be used to evaluate whether there exist interactions between the covariates and the controlling variables,and if any,in what form.A Bayesian information criterion(BIC)-type criterion is applied to determine the structural dimension of the central partial mean subspace.Its consistency is also established.Numerical studies through simulations and real data examples are conducted to demonstrate the power and utility of the proposed semi-parametric approaches.