1,721,094 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
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
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
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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