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Over the last few years, there has been a rising demand for sub-metre accuracy (and higher) for navigation and surveying using signals from Global Navigation Satellite Systems (GNSS). To meet this rising demand, many precise positioning techniques and algorithms using the carrier-phase observable have been developed. Currently, high accuracy Real-Time Kinematic (RTK) positioning is possible using relative or differential techniques which require one GNSS user receiver and at least one other as the reference (known) station within a certain distance from the user. Unlike these conventional differential positioning techniques, Precise Point Positioning (PPP) is based on processing carrier phase observations from only one GNSS receiver. This is more cost-effective as it removes the need for reference receivers and therefore, is not limited by baseline length. However, errors mitigated by ‘differencing’ in conventional methods must be modelled accurately and reliably for PPP. This thesis develops a PPP software platform in Matlab code and uses it to investigate the state-of-the-art PPP algorithms and develop enhancements. Specifically, it is well documented that conventional PPP algorithms suffer from long convergence periods ranging from thirty minutes (for static users) to hours (for dynamic users). Therefore, to achieve fast convergence, two approaches are developed in this thesis. Firstly, a combination of the state-of-the-art GNSS error models and new algorithms for measurement weighting, management of receiver clock jumps and assignment of a dynamic covariance factor, are exploited. Secondly, based on the results of the analysis of the quantitative relationships between the PPP convergence and each of the residual measurement noise level and satellite geometry, a strategy for the selection of satellites (GPS and GALILEO) for PPP is developed and exploited. Tests using 24 hours of real data show that the two developments above contribute to the realisation of static PPP positioning accuracies of 40 cm (3D, 100%) within a convergence time of 20 minutes. Furthermore, based on simulated data, the same accuracy is achieved in kinematic mode but within a convergence time of one hour. These levels of performance represent significant improvements over the state-of-the-art (i.e. convergence time of twenty minutes instead of thirty for static users and one hour instead of hours for dynamic users). The potential of the use of multiple frequencies from modernised GPS and GALILEO on float ambiguity PPP is demonstrated with simulated data, and shown to have the potential to offer significant improvement in the availability of PPP in difficult user environments such as urban areas. Finally, the thesis addresses the potential application of PPP for mission (e.g. safety critical) applications and the need for integrity monitoring. An existing Carrier-phase Receiver Autonomous Integrity Monitoring (CRAIM) algorithm is implemented and shown to have the potential to protect PPP users against abnormally large errors.

In this study, we investigate models for the prediction of match outcome. These models are then used to aid decision-making. In particular, we consider batting strategy in test cricket. This model provides decision support for a team that is aiming to set a target at declaration. We also develop a measure of the importance of a match in a tournament. Such a measure may be of use in tournament design. Decision-making on the timing of a declaration in test cricket is considered using match outcome probabilities given the state of a game. Logistic regression is used to model the effect of covariates, target set and overs remaining, on match outcome probabilities. This approach is then extended to establish batting strategy by considering run rate and the distribution of runs scored during a partnership. A decision tool for batting strategy towards a target aimed for is established. The importance of a particular match in a tournament is measured given the outcomes of all other matches. This method is illustrated for the English Premiership. Match importance is calculated with respect to winning the Championship, relegation from Premiership, qualifying for the UEFA Champions League and prize money. Match outcome probabilities for the match of interest are estimated using an ordinal logistic regression model. Covariates that represent the short and long term performance of the competing teams are used in this prediction model. This thesis makes the following contributions regarding the application of statistical methods in sport. A new quantitative approach that considers the optimum declaration "time" in test cricket is developed. We consider this modelling of fundamental playing strategy to be novel. We find that a zero-inflated negative binomial distribution is a good model for the distribution of runs scored in test cricket. The match importance measure that we describe extends an existing definition. The match outcome model we use for calculating match importance considers novel covariates related to the recent results of teams.

Using 818 pb[-]1 of e+ e[-] collisions recorded at the [psi](3770) resonance with the CLEO-c detector at CESR, we determine absolute hadronic branching fractions of charged and neutral D mesons using a double tag technique. Among measurements for three D0 and six D+ modes, we obtain reference branching fractions B(D0 [RIGHTWARDS ARROW] K [-] [pi]+ ) = (3.906 ± 0.021 ± 0.062)% and B(D+ [RIGHTWARDS ARROW] K [-] [pi]+ [pi]+ ) = (9.157 ± 0.059 ± 0.125)%, where the first uncertainty is statistical, the second is systematic errors. Using an independent determination of the integrated luminosity, we also extract the cross sections [sigma](e+ e[-] [RIGHTWARDS ARROW] D0 D0 ) = (3.650 ± 0.017 ± 0.083) nb and [sigma](e+ e[-] [RIGHTWARDS ARROW] D+ D[-] ) = (2.920 ± 0.018 ± 0.062) nb at a center of mass energy, Ecm = 3774 ± 1 MeV.