Baibing Li

Loughborough University | Lough · School of Business and Economics

This paper contributes to the literature by developing a new methodology, termed the beta index, for measuring liquidity commonality in financial markets which is derived from the dynamics of liquidity co-movements. We show that computing the beta index is a straightforward process. In addition, not only is the proposed beta index more efficient in...

In practice, additional knowledge about the target to be tracked, other than its fundamental dynamics, can often be modelled as a set of soft constraints and utilised in a filtering process to improve the tracking performance. This paper develops a general approach to the modelling of soft inequality constraints, and investigates particle filtering...

It is expected that soon there will be a significant number of unmanned aerial vehicles (UAVs) operating side-by-side with manned civil aircraft in national airspace systems. To be able to integrate UAVs safely with civil traffic, a number of challenges must be overcome first. This study investigates situational awareness of UAVs' autonomous taxiin...

This paper presents a new approach to discrete choice analysis for risky prospects. Conventional discrete choice analysis focuses on riskless prospects and does not deal with the scenario where the alternatives that the decision-makers choose from are associated with risk. In this paper, we investigate decision-makers’ risk perception and choice be...

Risks associated with international investments such as the foreign exchange (FX) exposure have recently gained increasing attention, especially those originating from the liquidity conditions of the FX market after the financial crisis of 2007-2008. This paper investigates whether hedge funds time the liquidity in the FX market and to what extent...

This paper shows a sharp contrast between theoretical predictions of merger negotiations when takeover markup and runup are measured in dollar vs rate terms. It argues that the empirical tests by an influential study cannot reject the hypothesis of a costly feedback loop as the authors claim. Using markup and runup in standardized dollar terms, it...

For nonlinear non-Gaussian stochastic dynamic systems with inequality state constraints, this paper presents an efficient particle filtering algorithm, constrained auxiliary particle filtering algorithm. To deal with the state constraints, the proposed algorithm probabilistically selects particles such that those particles far away from the feasibl...

This two-part paper presents a new approach to stochastic dynamic modeling for vehicle platoons. Part I develops a vehicle platoon model to capture the dynamics of vehicles' grouping behavior and proposes an online platoon recognition algorithm. On the basis of the developed platoon model, Part II investigates various important characteristics of v...

A vehicle platoon is a group of vehicles traveling together at approximately the same speed. Traffic platooning is an important phenomenon that can substantially increase the capacity of roads. This two-part paper presents a new approach to stochastic dynamic modeling for vehicle platoons. In part I, we develop a vehicle platoon model with two inte...

Researchers have long been puzzled by ICT's (Information and Communication Technology) contributions towards (productivity) growth. This paper investigates and reveals the multi-facets of ICT productivity and the mechanism through which ICT affects productivity by bringing all the distinct streams of existing findings together. In particular, we de...

This paper investigates the problem of ground vehicle tracking with ground-moving target indicator (GMTI) radar. In practice, the movement of ground vehicles may involve several different maneuvering types (acceleration, deceleration, standstill, etc.). Consequently, the GMTI radar may lose measurements when the radial velocity of the ground vehicl...

As unmanned aerial vehicles (UAVs) become widely used in various civil applications, many civil aerodromes are being transformed into a hybrid environment for both manned and unmanned aircraft. In order to make these hybrid aerodromes operate safely and efficiently, the autonomous taxiing system of UAVs that adapts to the dynamic environment has no...

This paper considers the situation awareness function associated with an unmanned aerial vehicle arriving at an uncontrolled airfield. Given no air traffic control service available within such a terminal area, the unmanned aerial vehicle needs to establish a good level of situation awareness by using its onboard sensors to detect and track other t...

We investigate the liquidity timing skills of debt-oriented hedge funds following the 2008 credit crisis, which demonstrated the importance of understanding liquidity conditions to manage the market exposure of investments. We base the analysis on the estimated co-movements of fixed income and equity market liquidity. Our findings, which are statis...

The conventional disturbance observers for discrete-time linear stochastic systems assume that the system states are fully estimable and the disturbance estimate is dependent on the estimated system states, hereafter termed Full-Order Disturbance Observers (FODOs). This paper investigates the design of Reduced-Order Disturbance Observers (RODOs) wh...

In this paper, a map-enhanced method is proposed for vision-based taxiway centreline extraction, which is a prerequisite of autonomous visual navigation systems for unmanned aerial vehicles. Comparing with other sensors, cameras are able to provide richer information. Consequently, vision based navigations have been intensively studied in the recen...

This paper investigates the problem of state estimation of nonlinear systems with nonlinear equality constraints. By treating the nonlinear equality constraints as part of the new coordinate, a diffeomorphism is found such that an unconstrained reduced-order system is obtained. The existing unconstrained estimators can be applied to obtain the redu...

This paper investigates the problem of simultaneous state and input estimation (SSIE) for discrete-time linear stochastic systems when the information on the inputs is partially available. To incorporate the partial information on the inputs, matrix manipulation is used to obtain an equivalent system with reduced-order inputs. Then Bayesian inferen...

In this paper, a disturbance observer is proposed for nonlinear systems with high order disturbance, where not only disturbance but also its high order derivatives are estimated. The relationship of the proposed observer with the existing results is discussed. Then, the result is further extended to the case of minimal order output-based disturbanc...

For linear stochastic time-varying systems, we investigate the properties of the Kalman filter with partially observed inputs. We first establish the existence condition of a general linear filter when the unknown inputs are partially observed. Then we examine the optimality of the Kalman filter with partially observed inputs. Finally, on the basis...

Total least squares (TLS) technique is introduced to dynamically identify intersection traffic flow patterns when both of the observations for entering and exiting vehicles have random measurement errors. An algorithm of dynamic TLS estimations of intersection traffic flow patterns is proposed. Simulation experiments show that it can improve estima...

This document contains the Matlab code and the datasets used in the clustering-function-based method.

This paper investigates the problem of state estimation for discrete-time stochastic linear systems, where additional knowledge on the unknown inputs is available at an aggregate level and the knowledge on the missing measurements can be described by a known stochastic distribution. Firstly, the available knowledge on the unknown inputs and the sta...

This paper investigates the differences between disturbance observer techniques and residual generation method in fault diagnosis. We firstly point out the possible limitations of residual based approach compared with fault estimation based approach through sensors fault diagnosis of a linear motor driving system. On the one hand, it may produce a...

Pedestrians who cross streets during the red-man phase of traffic light signals expose themselves to safety and health hazards and hence are considered to be at risk. Pedestrians’ street-crossing behavior is in general the outcome of interaction between pedestrians and vehicles: the gaps between vehicles provide an opportunity for pedestrians to cr...

For the purposes of both traffic-light control and the design of roadway layouts, it is important to understand pedestrian street-crossing behavior because it is not only crucial for improving pedestrian safety but also helps to optimize vehicle flow. This paper explores the mechanism of pedestrian street crossings during the red-man phase of traff...

For linear stochastic time-varying state space models with Gaussian noises, this paper investigates state estimation for the scenario where the input variables of the state equation are not fully observed but rather the input data are available only at an aggregate level. Unlike the existing filters for unknown inputs that are based on the approach...

In this paper, the problem of state estimation is considered for discrete-time stochastic linear systems subject to both partially observed inputs and multiple missing sensor measurements. First, the partially available information on the unknown inputs and the state equation are used to form the prior distribution of the state vector at each step....

This paper investigates the modelling for traffic densities with lane-change behaviour using the information provided by loop detectors. The existing studies on traffic density estimation for multi-lane roadways mainly focus on the scenario where either vehicles’ lane-change manoeuvres are not common or the lane-change pattern is time-invariant. Th...

Inductive loop detectors are widely deployed in strategic roadway networks. This paper investigates recursive estimation of traffic densities using the information provided by loop detectors. The existing studies for multilane roadways mainly focus on the scenario where vehicles' lane-change movements are not common and can be ignored. This paper,...

Vehicle time headway is an important traffic parameter. It affects roadway safety, capacity, and level of service. Single inductive loop detectors are widely deployed in road networks, supplying a wealth of information on the current status of traffic flow. In this paper, we perform Bayesian analysis to online estimate average vehicle time headway...

Single inductive loop detectors (ILDs), which provide online measurements of traffic volume and occupancy, are widely used devices in road systems. Because of the nature of traffic flow, fast estimation and forecasting of vehicular speed using the data collected by an ILD are crucial to online road traffic management. Here we formulate statistical...

The explosive growth of Internet retailing offers an excellent opportunity to collect online prices at a disaggregate (e.g., individual store and/or individual product) level over time and to investigate the evolution of Internet markets. In this paper, we generalize the results obtained in existing static analyses and develop two random coefficien...

The multinomial logit model in discrete choice analysis is widely used in transport research. It has long been known that the Gumbel distribution forms the basis of the multinomial logit model. Although the Gumbel distribution is a good approximation in some applications such as route choice problems, it is chosen mainly for mathematical convenienc...

Vehicle time headway is one of the most fundamental microscopic characteristics of traffic flow. It affects roadway safety, capacity, and level of service. An estimated average time headway can provide an input to intelligent transportation systems for the online monitoring of roadway safety. Single inductive loop detectors are widely installed dev...

Drawing on exit, voice, loyalty and neglect (EVLN) literature, this study examines direct and interactive associations between organizational-level commitment and team-level commitment and the use of EVLN by managers in India. The study is based on a survey of 200 managers and supervisors from seven Indian firms. The findings on the use of voice ar...

A dual-loop detector consists of two connected single-loop detectors placed several feet apart. Compared with a single-loop detector, it is able to provide more useful information on traffic flow with a higher precision. In this paper we investigate statistical inference for vehicle speed and vehicle length using dual-loop detector data. A Bayesian...

This paper investigates the recursive estimation of vehicular speed using the information provided by a single inductance loop detector (ILD). A statistical model for space-mean speed measured by an ILD is developed, upon which a Bayesian analysis is carried out to estimate vehicular speed. This results in a set of recursive formulae which is analy...

The key factor that complicates statistical inference for an origin-destination (O-D) matrix is that the problem per se is usually highly underspecified, with a large number of unknown entries but many fewer observations available for the estimation. In this paper, we investigate statistical inference for a transit route O-D matrix using on-off cou...

Dichotomous transformations for continuous outcomes are commonly used. In this paper, we investigate dichotomisation for statistical inference about odds ratios in a situation where two underlying distributions from which independent samples are drawn are skewed and unknown. Under some mild conditions it is shown that a suitable choice of the cutpo...

The aim of this paper is to present a two-stage partial least squares (PLS) methodology, for the monitoring of processes that are known to be affected by sources of variation that are an inherent part of routine operations. These sources, termed nuisance or confounding variation, will typically either mask the more subtle process changes that are o...

Investors in a market frequently update their diverse perceptions of the values of risky assets, thus invalidating the classic capital asset pricing model's (CAPM) assumption of complete agreement among investors. To accommodate information asymmetry and belief updating, we have developed an empirically testable information-adjusted CAPM, which sta...

Logistic regression analysis can provide an estimate of the odds ratio (OR), adjusted for a number of confounders. It is known that it is approximately equal to the associated adjusted relative risk (RR) if the incidence of an outcome of interest is rare in a cohort study or a clinical trial study. In this paper, we consider the incidence of an out...

The purpose of the paper is to present a new statistical approach to hierarchical cluster analysis with "n" objects measured on "p" variables. Motivated by the model of multivariate analysis of variance and the method of maximum likelihood, a clustering problem is formulated as a least squares optimization problem, simultaneously solving for both a...

Case-series analysis is used to estimate relative incidences of clinical events in defined time intervals after vaccination compared to a control period. It has advantages, in terms of both a reduction in data collection effort, because it uses only data on cases, and a reduction in the resultant variances of estimates, due to individuals being sel...

Sign eigenvectors for a real square matrix, A, are defined to be sign vectors for which all of its elements either retain the same signs or become to their opposite signs after the linear transformation A, where a sign vector is a vector with the elements equal to either 1 or -1. Existence of sign eigenvectors for symmetric positive semi-definite m...

Information on the origin-destination (OD) matrix of a transport network is a fundamental requirement in much transportation planning. A relatively inexpensive method for updating an OD matrix is to draw inference about the OD matrix based on a single observation of traffic flows on a specific set of network links, where the Bayesian approach is a...

A nested partial least-squares (PLS) algorithm is proposed for the modelling of non-linear systems in the presence of multicollinearity. The nested algorithm comprises both an inner and outer PLS algorithm. The objective of the outer algorithm is to extract those latent variables that will form the basis of the final application whilst the role of...

A regression methodology motivated by latent root regression has recently been proposed based on constrained principal component analysis (RM-CPCA). Currently, this approach is only applicable to situations where the number of observations exceeds the number of variables. The aim of this paper is to extend the RM-CPCA algorithm to the situation whe...

The detection of multiple influential cases in principal component analysis through case-deletion necessitates the investigation of a large number of combinations of observation cases to locate the influential cases. In this article, a graphical technique is proposed for the detection of multiple influential cases in principal component analysis ba...

The objective of this paper is to present a modified algorithm for the orthogonal signal correction (OSC) filter based on the approaches proposed by Wold, Antti, Lindgren and Öhman (Chemometrics Intell. Lab. Syst. 1998; 44: 175–185) and Fearn (Chemometrics Intell. Lab. Syst. 2000; 50: 47–52). An OSC filter consists of a trio of building blocks: wei...

Partial least squares (PLS) regression is a powerful and frequently applied technique in multivariate statistical process control when the process variables are highly correlated. Selection of the number of latent variables to build a representative model is an important issue. A metric frequently used by chemometricians for the determination of th...

It is known that total least squares (TLS) estimates are very sensitive to outliers. Therefore, identification of outliers is important for exploring appropriate model structures and determining reliable TLS estimates of parameters. In this paper, we investigate sensitivities of TLS estimates as observation data are perturbed, and then, based on pe...

For the cumulative distribution function (c.d.f.) of the F distribution, F(x;k,n), with associated degrees of freedom, k and n, a shrinking factor approximation (SFA), G(λkx;k), is proposed for large n and any fixed k, where G(x;k) is the chi-square c.d.f. with degrees of freedom, k, and λ=λ(kx;n) is the shrinking factor. Numerical analysis indicat...

Four formulations of principal component analysis in L1 norm were developed by Galpin and Hawkins (Comput. Statist. Data Anal. 5 (1987) 305), QPmax, QPmin, LPmax, and LPmin. Choulakian (Comput. Statist. Data Anal. 37 (2001)135) claimed that of the four formulations, only QPmax produces a non-trivial solution. The objective is to present counter-exa...

It is quite often in practice that observation information of traffic flows is incomplete due to failures of facilities or insufficient installment of sensors. In this paper, we investigate problems of dynamic identification of origin–destination matrices when traffic counts are unavailable in some entrances and/or exits of a traffic system. To dea...

A general Box–Cox transformation method in multiple linear regressions is investigated. An algorithm is proposed to identify optimal general Box–Cox transformations based on kernel density estimation techniques. It is shown that for a multiple linear regression problem, the optimal general Box–Cox transformation can be derived through solving a mat...

Partial least squares (PLS) is a powerful and frequently applied technique for process modelling and monitoring when the data is highly correlated. In this paper, a Box–Tidwell transformation based PLS (BTPLS) algorithm is proposed to address the modelling of non-linear systems. The BTPLS algorithm provides a family of flexible regression models fo...

This paper presents a graphical technique for detecting influential cases in regression analysis. The idea is to decompose a diagnostic problem involving higher order dimensional regression problems, into a series of two-dimensional diagnostic sub-problems, such that the diagnoses of influential cases is undertaken by visually inspecting two-dimens...

A fast constrained recursive identification (CRI) algorithm is proposed to estimate intersection origin-destination (O-D) matrices dynamically. The basic idea of the CRI algorithm is to estimate intersection O-D matrices based on equality-constrained optimization and then to adjust them by Bell's correction (Bell, 1991a. The estimation of origin-de...

For ATM network traffic, a new approach based on the Kullback-Leibler (1959) information measure is proposed for stochastic system identification of packet traffic. This approach, equivalent to the maximum marginal likelihood estimate, can overcome the over-modeling problem discussed by De Cock and De Moor. (see Proceedings of ELISIPCO-98, 1998) su...

For Markov modulated Poisson process (MMPP) models, a statistical division (SD) based modeling approach is proposed for multimedia network traffic. Due to its exploiting information from observed data, the SD can make significant reductions of problem-scales and then lead to a fast algorithm in comparison with the previously proposed non-informed d...

Local influence on the eigenvalues of sample covariance matrices in principal components analysis is examined for a reasonable modification of Shi's (1997) perturbation scheme, The modification is suggested for samples from populations with both unknown mean vector and covariance matrix. While Shi's detection indexes (1997) consist of only quadrati...

A family of adaptive robust estimates which range from L1 to L2 estimates is suggested based on the symmetrical generalized logistic (SGL) distribution. Depending on whether existing outiiers and how serious for ihe outliers in sampling data, this adaptive SGL estimate family can automatically choose an L2 estimate, L1 estimate, or smoothed Huber e...

For the Student's t cumulative distribution function F(x; n) with n greater than or equal to 3 degrees of freedom, a corrected normal approximation, Phi(lambda x), is proposed as an extension of the well-known ordinary normal approximation Phi(x), where Phi(x) is the standard normal cumulative distribution and lambda=lambda(x,n) is a shrinking fact...

In this paper, we obtain a new approximation of the Student's t distribution by using the symmetric generalized logistic (SGL) distribution function. The error of this approximation is shown to be 0(1/n2 )where nis the degrees of freedom of thetdistribution. In comparison to similar approximations by George and Ojo and George et al. (1986), this ne...

Through a game theory model, the inter-relationship among taxation, government subsidy for consumers and the output levels in business firms is investigated. It is shown that, for different values taken by the subsidy factor in its parameter space, there exist three types of change pattern for the outputs levels when the rate of the sales tax is in...

This paper attempts to bridge the gap between rational expectations equilibrium analysis and the standard CAPM. It finds that although the market portfolio is not mean-variance efficient under asymmetric information, the security market capital line still exists. Moreover, this security market line is determined by the Information-adjusted Average...

Stochastic approximation algorithms are introduced in this paper toobtain a recursive identification with a recursive estimation of covariance matrix(RIREC) for origin-destination (O-D) matrices. This approach is quite robust onerror disturbances due to its independence on assumption of covariancestructures of errors. Simulation comparison for robu...