11,555 research outputs found
Generalised Dynamic Nonlinear Time Series Regression and Forecasting: Theory with Applications
No full textThis thesis aims to develop a series of nonlinear time series models for analysing count data, especially to overcome the “curse of dimensionality” for high and ultra-high dimensions. This is of particular needs for big data analysis in applications to discrete-valued outcome events, such as financial market direction, infected patients number in epidemiology and etc., where the nature of data is often unknown.In contrast to time series for continuous responses, where numerous related studies are available, literature paid scant attention to discrete-valued time series estimation and forecasting. Existing studies are developed based on the extension of classic AutoRegressive Moving Averge model (ARMA). To better capture the relationship between response and exogenous variables, we have proposed a semi-parametric procedure called the “ Generalised Model Averaging MArginal nonlinear Regressions (GMAMaR) and showed the uniform consistency for local maximum likelihood estimation of one dimensional non-parametric local linear estimation. The asymptotic properties of the procedure are established under mild conditions on the time series observations that are of β-mixing property. This model has overcame the “curse of dimensionality” by taking the advantage ofcheap computational cost of low dimensional estimation and the idea of model averaging to approximate the true estimates.In particular, to deal with the popular binary classification problem, we study a special case of logistic regression, namely “Model Averaging MArginal nonlinear LOgistic Regressions (MAMaLOR). This is the case where binary outcome is considered. The performance of our proposed model is superior when compared to conventional method with numerical examples.We notice another problem when facing big data that only a few of them are truly useful in explaining the responses out of hundreds and thousands exogenous variables. Thus, we propose a penalise maximum likelihood estimation for variableselection combined with our developed model by utilising adaptive LASSO as a tool. A new computational procedure is also suggested to solve the proposed penalised likelihood estimation. By extracting important information from data, theperformance of our proposed methods is improved significantly both in estimation and in prediction.Last but not least, with the on-going event of COVID-19 in the UK, we further consider the spatial effects along with temporal dependency. The idea is thus to extend time series analysis to the domain of spatio-temporal modelling. We utiliseproposed model to investigate impacts of micro variables of the implementation of lockdown on the daily number of confirmed cases. The results are consistent with the consensus of epidemiology studies, and deeper understandings of how toadapt and prioritise the policies in the combat of epidemic are also provided.To conclude, the proposed series of nonlinear time series models show great potential in the context of discrete-valued events. While providing a more accurate estimation and prediction, the models also offer a better interpretability and deeper understanding of the relationships between response and potential factors. We hope to demonstrate that this thesis thus contribute to the development of this area, and could be further extended to the area of sptio-temporal and other areasof applications
Uniform consistency for local fitting of time series non-parametric regression allowing for discrete-valued response
No full textLocal linear kernel fitting is a popular nonparametric technique for modelling nonlinear time series data. Investigations into it, although extensively made for continuousvalued case, are still rare for the time series that are discrete-valued. In this paper, we propose and develop the uniform consistency of local linear maximum likelihood (LLML) fitting for time series regression allowing response to be discrete-valued under β-mixing dependence condition. Specifically, the uniform consistency of LLML estimators is established under time series conditional exponential family distributions with aid of a beta-mixing empirical process through local estimating equations. The rate of convergence is also provided under mild conditions. Performances of the proposed method are demonstrated by a Monte-Carlo simulation study and an application to COVID-19 data.</p
Speculative trading, price pressure and overvaluation
No full textPrior theoretical studies (e.g., Harrison and Kreps, 1978) show that investors pay prices over their valuation of assets if potential buyers are willing to pay even more in the future. This study provides supporting evidence by focusing on the Hong Kong “through train” scheme in August 2007, through which mainland Chinese investors were allowed to directly invest in Hong Kong market, but the decision was reassessed (actually suspended) in November 2007. Our findings show that Hong Kong stocks exhibit excess trading volume associated with the two announcements, and stocks are traded higher after the launch-decision day and lower after the reassessment-decision day
Semiparametric averaging of nonlinear marginal logistic regressions and forecasting for time series classification
No full textBinary classification is an important issue in many applications but mostly studied for independent data in the literature. A binary time series classification is investigated by proposing a semiparametric procedure named “Model Averaging nonlinear MArginal LOgistic Regressions” (MAMaLoR) for binary time series data based on the time series information of predictor variables. The procedure involves approximating the logistic multivariate conditional regression function by combining low-dimensional non-parametric nonlinear marginal logistic regressions, in the sense of Kullback-Leibler distance. A time series conditional likelihood method is suggested for estimating the optimal averaging weights together with local maximum likelihood estimations of the nonparametric marginal time series logistic (auto)regressions. The asymptotic properties of the procedure are established under mild conditions on the time series observations that are of β-mixing property. The procedure is less computationally demanding and can avoid the “curse of dimensionality” for, and be easily applied to, high dimensional lagged information based nonlinear time series classification forecasting. The performances of the procedure are further confirmed both by Monte-Carlo simulation and an empirical study for market moving direction forecasting of the financial FTSE 100 index data.</p
Niu peng za yi
No full textBen shu nei rong bao kuo niu peng za yi,Shu zhai za lu,Bing ta za ji deng bu fen,Bao kuo"yuan qi","cong she jiao yun dong tan qi","dui hao ru zuo","kuai huo ban nian","zi ji tiao chu lai"den
The advertisement calls of Quasipaa shini (Ahl, 1930) (Anura: Dicroglossidae)
No full textKong, Shen Shen, Zheng, Rong Quan, Zhang, Qi Peng (2016): The advertisement calls of Quasipaa shini (Ahl, 1930) (Anura: Dicroglossidae). Zootaxa 4205 (1): 87-89, DOI: http://doi.org/10.11646/zootaxa.4205.1.
sj-docx-1-aut-10.1177_13623613221138687 – Supplemental material for Categorical perception of Mandarin lexical tones in language-delayed autistic children
No full textSupplemental material, sj-docx-1-aut-10.1177_13623613221138687 for Categorical perception of Mandarin lexical tones in language-delayed autistic children by Yicheng Rong, Yi Weng, Fei Chen and Gang Peng in Autism</p
On a location-wide semiparametric analysis of spatio-temporal dynamics of the COVID-19 daily new cases in the UK
No full textThe COVID-19 pandemic has impacted the way people live worldwide, including the UK. In this paper, we have proposed a location-wide semiparametric spatio-temporal modelling method for analysis of the dynamics of a spatio-temporal daily confirmed number of COVID-19 cases at 367 local authority areas in the UK. Estimation of the spatio-temporal model for the count data taking into account both the nonlinear time trend and the spatial neighbouring effect is developed. With the aid of variable selection, it is empirically shown that the proposed model performs well in application to the UK COVID-19 data estimation and prediction. The empirically extracted information from the data provides some new insights into what are the key factors contributing to the confirmed daily number of cases at different locations. Itis found that the success of interventions varies depending on location, subject to population, medical resource and role in the national or international transportation network. Our finding also shows that the neighbouring effects are significant, and hence limiting public transportation is always effective to control the spread of the pandemic by reducing contacts. Furthermore, it is empirically noted that the media effects are significant, which may be due to the promotion of self-protection awareness in controlling the spread of the pandemic
Toward a Motivation Model of Pragmatics/ Rong Chen.
No full textIn English.With the "discursive turn" has come a distrust - a complete rejection by some - of theories that seek deeper reasons for surface phenomena. Rong Chen argues that this distrust, with its accompanying overemphasis on specificity and fluidity of linguistic meaning and social values, is unwarranted and unhelpful. Drawing on insights from social theories and various strands of pragmatics, he proposes a motivation model of pragmatics (MMP), contending that language use can be adequately, coherently, and elegantly studied via the motivation behind it in its varied and dynamic contexts. The model, with its well-laid out components, is then applied to (im)politeness research, cross-cultural pragmatics, diachronic pragmatics, discourse and genre analysis, conversation analysis, identity construction, and the study of metaphor, sarcasm, parody, and lying. MMP is thus a framework aimed at accounting for fluidity with stable notions, specificity with general principles, and differences with similar underlying factors. As such, the book should appeal to students of pragmatics, (im)politeness, conversation analysis, sociolinguistics, applied linguistics, communication, sociology, and psychology.Frontmatter -- Foreword -- Contents -- List of figures -- List of tables -- Chapter 1 Pragmatics then and now -- Chapter 2 A motivation model of pragmatics (MMP) -- Chapter 3 MMP and (im)politeness -- Chapter 4 MMP and cross-/intercultural variation -- Chapter 5 MMP and diachronic pragmatics -- Chapter 6 MMP and discourse -- Chapter 7 MMP and metaphor -- Chapter 8 MMP and the non-literal -- Afterword -- References -- Appendix -- Subject index -- Author index1 online resource (XIII, 333 p.)
Supplemental Material, sj-docx-1-ptd-10.1177_08968608221088638 - Burden of kidney disease among patients with peritoneal dialysis versus conventional in-centre haemodialysis: A randomised, non-inferiority trial
No full textSupplemental Material, sj-docx-1-ptd-10.1177_08968608221088638 for Burden of kidney disease among patients with peritoneal dialysis versus conventional in-centre haemodialysis: A randomised, non-inferiority trial by Li Fan, Xiao Yang, Qinkai Chen, Hao Zhang, Jianqin Wang, Menghua Chen, Hui Peng, Zhaohui Ni, Jianxin Wan, Hongtao Yang, Yun Li, Li Wang, Ai Peng, Hongli Lin, Jinyuan Zhang, Huaying Shen, Fei Xiong, Yongcheng He, Yan Zha, Minyan Xie, Jundong Jiao, Gengru Jiang, Xunhuan Zheng, Jun Xiao, Rong Rong, Jiaqi Qian and Xueqing Yu in Peritoneal Dialysis International</p
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