A review of the scientific literature revealed a lack of information regarding the integrated daily activity levels of low-risk pregnant women in contemporary Western society. A prospective, longitudinal study was therefore undertaken to (i) assess the impact of low-risk pregnancy on the daily activity levels of healthy, British primigravid women and (ii) examine the relationship between total maternal daily activity level and pregnancy outcome. The best combination of methods to measure daily activity levels during pregnancy was considered to be a subjective self-report measure used in conjunction with an objective ambulatory activity monitor. These methods were developed and were demonstrated to be both reliable and valid in non-pregnant women. However, the study identified some unique problems in using activity monitors in pregnant women. These problems emanated both from women's reluctance to wear an activity monitor when pregnant and from a need to measure extremely low levels of activity in late gestation. Both data from the ambulatory monitor and the new activity questionnaire demonstrated a overall decrease in mean maternal daily activity levels between 25 & 38 weeks gestation (n=51; p<0.01). This decline masked different maternal responses in different activity domains. The mean occupational activity ratios of women working full-time declined steadily between 16 & 34 weeks gestation (n=25, p<0.01). Women's working hours and the more flexible elements of their work were reduced whilst the frequency of work breaks increased. Between 25 & 38 weeks gestation, mean recreational activity ratios also declined (n=50, p<0.05). Participation in structured sports and exercise ceased and increasing amounts of time were spent within the home. In contrast, mean overall domestic activity ratios were maintained. Between 16 & 38 weeks gestation mean nocturnal activity ratios increased steadily (n=47; p=0.01) To maintain waking activity during pregnancy, women actively engaged in a number of different balancing strategies. These strategies comprised monitoring, prioritising, pacing and forward planning. Despite the changes that occurred in maternal activity behaviour, one of the strongest and most consistent predictors of maternal activity behaviour during pregnancy was that of maternal activity behaviour prior to pregnancy. Occupational activity levels pre-pregnancy were independently associated with maternal daily activity levels at 12,16 & 25 weeks gestation (p=0.004-0.020). Self-efficacy was the only significant predictor of the change in maternal daily activity levels between 25 & 38 weeks gestation (p=0.013). The women who reduced their activity the most were likely to be those individuals who had more difficulty in overcoming perceived barriers to physical activity participation. Five main barriers to physical activity were identified: (i) the physical symptoms of pregnancy (ii) the effect of outside influences (iii) a lack of motivation (iv) a low maternal body image and (v) a lack of time and/or appropriate facilities. Findings suggested that maternal daily activity may impact significantly on pregnancy outcome. Higher maternal daily activity at 16 weeks gestation was independently associated with a lower incidence of emergency caesarean section (p<0.05). Higher maternal daily activity at 38 weeks gestation was independently associated with a higher incidence of an induction of labour (p<0.05). Total daily activity at 25 & 34 weeks gestation was independently and negatively associated with infant birthweight (p<0.05).

The key to the co-rotational (CR) finite element is the separation between the rigid body motion and the deformational motion. It is this separation which makes it superior to other methods in the analysis of large displacement problems. Since the dynamic analysis of a guyed transmission line system contains large displacements from the vibration of the cable, it is considered appropriate to utilize the technique in the analysis. This thesis re-formulates and simplifies the CR method for such a purpose. Numerical tests show that the time step required for convergence in the present technique is ten times less than that is required for convergence in ANSYS. In the construction of the equation for the prediction of the longitudinal load factor (LLF) for the A402-M guyed transmission line due to cable break events, the tower is modelled using a simplified model of a detailed lattice tower. The simplified model considers latticed tower segment as an equivalent beam segment. The use of the simplified model enables to perform the broken wire dynamic analysis of the ten-span transmission line system within a day or two on a personal computer. Two initiating events are considered: all conductors on one arm break and all cables in one span break. Based on the analysis results, it is found that the LLFs for the all cables break event for the A402-M tower are 5% less than that calculated using the EPRI equation. It is therefore recommended that either the LLFs derived from the EPRI equation or from the proposed equation be used in the design of a guyed transmission tower for the broken wire event. The developed procedure can also be used to predict the LLF for the other type transmission line systems.

Longitudinal data arise commonly in many fields including public health studies and survey sampling. Valid inference methods for longitudinal data are of great importance in scientific researches. In longitudinal studies, data collection are often designed to follow all the interested information on individuals at scheduled times. The analysis in longitudinal studies usually focuses on how the data change over time and how they are associated with certain risk factors or covariates. Various statistical models and methods have been developed over the past few decades. However, these methods could become invalid when data possess additional features. First of all, incompleteness of data presents considerable complications to standard modeling and inference methods. Although we hope each individual completes all of the scheduled measurements without any absence, missing observations occur commonly in longitudinal studies. It has been documented that biased results could arise if such a feature is not properly accounted for in the analysis. There has been a large body of methods in the literature on handling missingness arising either from response components or covariate variables, but relatively little attention has been directed to addressing missingness in both response and covariate variables simultaneously. Important reasons for the sparsity of the research on this topic may be attributed to substantially increased complexity of modeling and computational difficulties. In Chapter 2 and Chapter 3 of the thesis, I develop methods to handle incomplete longitudinal data using the pairwise likelihood formulation. The proposed methods can handle longitudinal data with missing observations in both response and covariate variables. A unified framework is invoked to accommodate various types of missing data patterns. The performance of the proposed methods is carefully assessed under a variety of circumstances. In particular, issues on efficiency and robustness are investigated. Longitudinal survey data from the National Population Health Study are analyzed with the proposed methods. The other difficulty in longitudinal data is model selection. Incorporating a large number of irrelevant covariates to the model may result in computation, interpretation and prediction difficulties, thus selecting parsimonious models are typically desirable. In particular, the penalized likelihood method is commonly employed for this purpose. However, when we apply the penalized likelihood approach in longitudinal studies, it may involve high dimensional integrals which are computationally expensive. We propose an alternative method using the composite likelihood formulation. Formulation of composite likelihood requires only a partial structure of the correlated data such as marginal or pairwise distributions. This strategy shows modeling tractability and computational cheapness in model selection. Therefore, in Chapter 4 of this thesis, I propose a composite likelihood approach with penalized function to handle the model selection issue. In practice, we often face the model selection problem not only from choosing proper covariates for regression predictor, but also from the component of random effects. Furthermore, the specification of random effects distribution could be crucial to maintain the validity of statistical inference. Thus, the discussion on selecting both covariates and random effects as well as misspecification of random effects are also included in Chapter 4. Chapter 5 of this thesis mainly addresses the joint features of missingness and model selection. I propose a specific composite likelihood method to handle this issue. A typical advantage of the approach is that the inference procedure does not involve explicit missing process assumptions and nuisance parameters estimation.

Informally a discrete time series is a set of repeated and, normally, equally spaced observations from the same process over time. The statistical analysis of time series has two functions: to understand better the generating process underlying the time series, and to forecast future values. The first analytical methods developed were based upon linear series. A linear series can be represented as a linear function of its own past and current values and the past and current values of some noise process, which can be interpreted as the innovations to the system. A non-linear series has a generally more complex structure that depends upon non-linear interactions between its past and current values and the sequence of innovations. Existing linear statistical methods can only approximate non-linear series. As there is evidence to show that non-linear series are common in real life, two important problems are to detect and then to classify non-linearity. In moving from a linear to a non-linear structure the choice of possible models has moved from a countably infinite to an uncountably infinite set. Hence the need for methods that not only detect non-linearity, but classify the non-linear relationship between the past and current values and innovations. The third order moment is the expectation of the product of three series values lagged in time. The bispectrum is the double Fourier transform of the third order moment. Both statistics are useful tools for eliciting information on non-linear time series. There are concerns with the assumption of asymptotic independence between the values of the bispectrum estimate used by an existing test of non-linearity. We develop a method with a greater power than this existing method to detect non-linear series by using a model-based bootstrap. Further we show how patterns in the bispectrum are useful for classifying the locations of the non-linear interactions. To understand better tests of non-linearity and related inference, we investigate the variance of two estimates of the bispectrum. The two estimates are shown to have different inferential properties. One estimate is generally better able than the other to detect non-linearity and give information on the location of the non-linear interactions. The third order moment is statistically equivalent to the bispectrum. A particular estimate of the bispectrum is the double Fourier transform of all the estimated third order moment values in a specified region. When using the third order moment to test for non-linearity we can examine any subset of these values in the specified region. Hence an advantage to using the third order moment, instead of the bispectrum, when testing for non-linearity is a greater flexibility in the range of values selected. We show an improved test for non-linearity over the bispectrum-based test, using a reduced set of the third order moment and a phase scrambling-based bootstrap. Time series can often be observed in a multiple or repeated form, such as the exchange rate between a set of currencies. There is then interest in summarising the common features of the grouped series. An existing linear method based on the spectrum assumes that an observed series (within a population) can be described as a common population spectrum perturbed by an individual effect. The observational noise in the spectrum is modelled using the known asymptotic properties of the spectral estimate. By modelling (and then removing) the individual effects and noise, the method summarises the population linear characteristics through the spectrum. We modify and then extend this method to summarise the common features of the underlying non-linear generating process of a set of repeated time series using the bispectrum normalised by the spectrum.

Diss. (sammanfattning) Umeå : Univ., 2005. / Härtill 4 uppsatser.

Thesis (M.S.)--University of Michigan, 1969.

Thesis (M.S.)--University of Michigan, 1969.

Thesis (doctoral)--Umeå Universitet, 1986. / Extra t.p. with thesis statement inserted. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Supervisor: Laura M. Stapleton. Vita. Includes bibliographical references.

Thesis (doctoral)--Umeå Universitet, 1986. / Extra t.p. with thesis statement inserted. Includes bibliographical references.