Adjustments for Nonresponse, Sample Quality Indicators, and Nonresponse Error in a Total Survey Error Context

The decline in response rates in surveys of the general population is regarded by many researchers as one of the greatest threats to contemporary surveys. Much research has focused on the consequences of nonresponse. However, because the true values for the non-respondents are rarely known, it is difficult to estimate the magnitude of nonresponse bias or to develop effective methods for predicting and adjusting for nonresponse bias. This research uses two datasets that have records on each person in the frame to evaluate the effectiveness of adjustment methods aiming to correct nonresponse bias, to study indicators of sample quality, and to examine the relative magnitude of nonresponse bias under different modes. The results suggest that both response propensity weighting and GREG weighting, are not effective in reducing nonresponse bias present in the study data. There are some reductions in error, but the reductions are limited. The comparison between response propensity weighting and GREG weighting shows that with the same set of auxiliary variables, the choice between response propensity weighting and GREG weighting makes little difference. The evaluation of the R-indicators and the penalized R-indicators using the study datasets and from a simulation study suggests that the penalized R-indicators perform better than the R-indicators in terms of assessing sample quality. The penalized R-indicator shows a pattern that has a better match to the pattern for the estimated biases than the R-indicator does. Finally, the comparison of nonresponse bias to other types of errors finds that nonresponse bias in these two data sets may be larger than sampling error and coverage bias, but measurement bias can be bigger in turn than nonresponse bias, at least for sensitive questions. And postsurvey adjustments do not result in substantial reduction in the total survey error. We reach the conclusion that 1) efforts put into dealing with nonresponse bias are warranted; 2) the effectiveness of weighting adjustments for nonresponse depends on the availability and quality of the auxiliary variables, and 3) the penalized R-indicator may be more helpful in monitoring the quality of the survey than the R-indicator

Convergence on Gauss-Seidel iterative methods for linear systems with general H-matrices

It is well known that as a famous type of iterative methods in numerical linear algebra, Gauss-Seidel iterative methods are convergent for linear systems with strictly or irreducibly diagonally dominant matrices, invertible $H-$matrices (generalized strictly diagonally dominant matrices) and Hermitian positive definite matrices. But, the same is not necessarily true for linear systems with nonstrictly diagonally dominant matrices and general $H-$matrices. This paper firstly proposes some necessary and sufficient conditions for convergence on Gauss-Seidel iterative methods to establish several new theoretical results on linear systems with nonstrictly diagonally dominant matrices and general $H-$matrices. Then, the convergence results on preconditioned Gauss-Seidel (PGS) iterative methods for general $H-$matrices are presented. Finally, some numerical examples are given to demonstrate the results obtained in this paper

A search for 95 GHz class I methanol masers in molecular outflows

We have observed a sample of 288 molecular outflow sources including 123 high-mass and 165 low-mass sources to search for class I methanol masers at 95 GHz transition and to investigate relationship between outflow characteristics and class I methanol maser emission with the PMO-13.7m radio telescope. Our survey detected 62 sources with 95 GHz methanol masers above 3$\sigma$ detection limit, which include 47 high-mass sources and 15 low-mass sources. Therefore the detection rate is 38% for high-mass outflow sources and 9% for low-mass outflow sources, suggesting that class I methanol maser is relatively easily excited in high-mass sources. There are 37 newly detected 95 GHz methanol masers (including 27 high-mass and 10 low-mass sources), 19 of which are newly identified (i.e. first identification) class I methanol masers (including 13 high-mass and 6 low-mass sources). Statistical analysis for the distributions of maser detections with the outflow parameters reveals that the maser detection efficiency increases with outflow properties (e.g. mass, momentum, kinetic energy and mechanical luminosity of outflows etc.). Systematic investigations of relationships between the intrinsic luminosity of methanol maser and the outflow properties (including mass, momentum, kinetic energy, bolometric luminosity and mass loss rate of central stellar sources) indicate a positive correlations. This further supports that class I methanol masers are collisionally pumped and associated with shocks, where outflows interact with the surrounding ambient medium.Comment: 32 pages, 5 figures, accepted by Ap

Transport properties of dense deuterium-tritium plasmas

Consistent descriptions of the equation of states, and information about transport coefficients of deuterium-tritium mixture are demonstrated through quantum molecular dynamic (QMD) simulations (up to a density of 600 g/cm$^{3}$ and a temperature of $10^{4}$ eV). Diffusion coefficients and viscosity are compared with one component plasma model in different regimes from the strong coupled to the kinetic one. Electronic and radiative transport coefficients, which are compared with models currently used in hydrodynamic simulations of inertial confinement fusion, are evaluated up to 800 eV. The Lorentz number is also discussed from the highly degenerate to the intermediate region.Comment: 4 pages, 3 figure

Multiple Change-point Detection for piecewise stationary categorical time series

In this dissertation, we propose a fast yet consistent method for segmenting a piecewise stationary categorical-valued time series, with a finite unknown number of change-points in its autocovariance structure. To avoid loss of information, instead of arbitrarily assigning numerical numbers in analysis of the original time series, we focus on the multinomial process, which is derived by denoting each category of the original series as a unit vector. The corresponding multinomial process is then modeled by a nonparametric multivariate locally stationary wavelet process, where the piecewise constant autocovariance structure for any given variate is completely described by the wavelet periodograms for that variate at multiple scales and locations. Further, we propose a criterion that optimally selects the scalings and provides the generation of the trace statistics whose mean functions inherit the piecewise constancy. The resulting statistics will serve as input sequences for later segmentation. Change-point detection is accomplished by first examining the input sequence at each scale with the binary segmentation procedure, and then combining the detected breakpoints across scales. The consistency result of our method is established under certain conditions. In addition, several simulation studies and a real-data analysis of a DNA sequence are provided to demonstrate the viability of our methodology