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.

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.

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

An analysis of Chinese urban residents＇ consumer demand employing a dynamic semi-parametric panel data model

Using data for China for the years 1991 to 2005 by province and employing the semi- parametric panel data model estimation method developed by Horowitz （2004） and Henderson et al. （2006） and Hubler＇s non-parametric generalized method of moments （GMM） estimation （2005）, this article constructs a dynamic semi-parametric panel data model and describes the dynamic changing trajectory of the effect on consumption of income disparity among urban residents. Our findings show that there is a significant ＂ratchet effect＂ in the consumption of urban residents; that income disparity among urban residents has a clear negative influence on consumption; and that the trajectory of this influence shows a roughly bimodal curve.

Local Polynomial-Brunk Estimation in Semi-Parametric Monotone Errors-in-Variables Model with Right-Censored Data

This paper introduces a semi-parametric model with right-censored data and a monotone constraint on the nonparametrie part. The authors study the local linear estimators of the parametric coefficients and apply B-spline method to approximate the nonparametric part based on grouped data. The authors obtain the rates of convergence for parametric and nonparametric estimators. Moreover, the authors also prove that the nonparametric estimator is consistent at the boundary. At last, the authors investigate the finite sample performance of the estimation.

ASYMPTOTICS FOR THE DISTRIBUTION FUNCTION ESTIMATORS OF THE ERRORS IN SEMI-PARAMETRIC REGRESSION MODELS

This paper considers the convergence rates for nonparametric estimators of the error distribution in semi-parametric regression models. By establishing some general laws of the iterated logarithm, it shows that the rates of convergence of either the empirical distribution or a smoothed version of the empirical distribution function matches exactly the rates obtained for an independent sample from the error distribution.

Local Linear Estimation by TSLS with Variable Bandwidth for Semi-parametric Simultaneous Equation Models in Econometrics

Econometric simultaneous equation models play an important role in making economic policies, analyzing economic structure and economic forecasting. This paper presents local linear estimators by TSLS with variable bandwidth for every structural equation in semi-parametric simultaneous equation models in econometrics. The properties under large sample size were studied by using the asymptotic theory when all variables were random. The results show that the estimators of the parameters have consistency and asymptotic normality, and their convergence rates are equal to n^-1/2. And the estimator of the nonparametric function has the consistency and asymptotic normality in interior points and its rate of convergence is equal to the optimal convergence rate of the nonparametric function estimation.

基于半参数空间模型的空气污染与经济增长关系再检验

本文选取2001-2014年我国30个省份数据作为样本,考虑空气污染的空间自相关性,采用空间杜宾滞后模型(SDM)和半参数空间滞后模型实证检验经济增长与空气污染的非线性关系。结果表明:①我国空气污染存在显著的空间正相关性,高空气污染水平集聚地随时间推移呈现出"由西向东"的转移特征;②空气污染与经济增长存在一种震荡曲线形式的关系,并不完全吻合传统的EKC倒U型曲线形状,但震荡关系也符合EKC所描述的环境污染与经济增长的关系将长期存在的特征,说明了经济增长并不能自发解决空气污染问题;③半参数空间滞后模型的拟合优度高于普通参数模型,其刻画的空气污染与经济增长的非线性特征验证了前人对二者震荡关系的猜想,结果更为稳健、准确与有效。

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.

Probability Models of Fire Risk Based on Forest Fire Indices in Contrasting Climates over China

Fire weather indices have been widely applied to predict fire risk in many regions of the world. The objectives of this study were to establish fire risk probability models based on fire indices over different climatic regions in China. We linked the indices adopted in Canadian, US, and Australia with location, time, altitude, vegetation and fire characteristics during 1998-2007 in four regions using semi- parametric logistic （SPL） regression models. Different combinations of fire risk indices were selected as explanatory variables for specific regional probability model. SPL regression models of probability of fire ignition and large fire events were established to describe the non-linear relationship between fire risk indices and fire risk probabilities in the four regions. Graphs of observed versus estimated probabilities, fire risk maps, graphs of numbers of large fire events were produced from the probability models to assess the skill of these models. Fire ignition in all regions showed a significant link with altitude and NDVI. Indices of fuel moisture are important factors influencing fire occurrence in northern China. The fuel indices of organic material are significant indicators of fire risk in southern China. Besides the well skill of predicting fire risk, the probability models are a useful method to assess the utility of the fire risk indices in estimating fire events. The analysis presents some of the dynamics of climate-fire interactions and their value for management systems.

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.

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.

LACK-OF-FIT TESTS BASED ON WEIGHTED RATIO OF RESIDUALS AND VARIANCES

This article proposes a new lack-of-test based on the weighted ratio of residuals and variances for partially linear regression models. The large and small sampling properties of the proposed test are established. The testing procedure is illustrated via several examples. Simulation studies show that the testing procedures are powerful even in small samples. An application of the test to a real data set is presented.