Induced smoothing for rank regression with censored survival times

Adaptions of weighted rank regression to the accelerated failure time model for censored survival data have been successful in yielding asymptotically normal estimates and flexible weighting schemes to increase statistical efficiencies. However, for only one simple weighting scheme, Gehan or Wilcoxon weights, are estimating equations guaranteed to be monotone in parameter components, and even in this case are step functions, requiring the equivalent of linear programming for computation. The lack of smoothness makes standard error or covariance matrix estimation even more difficult. An induced smoothing technique overcame these difficulties in various problems involving monotone but pure jump estimating equations, including conventional rank regression. The present paper applies induced smoothing to the Gehan-Wilcoxon weighted rank regression for the accelerated failure time model, for the more difficult case of survival time data subject to censoring, where the inapplicability of permutation arguments necessitates a new method of estimating null variance of estimating functions. Smooth monotone parameter estimation and rapid, reliable standard error or covariance matrix estimation is obtained.

Efficient regression analysis with ranked-set sampling

This article is motivated by a lung cancer study where a regression model is involved and the response variable is too expensive to measure but the predictor variable can be measured easily with relatively negligible cost. This situation occurs quite often in medical studies, quantitative genetics, and ecological and environmental studies. In this article, by using the idea of ranked-set sampling (RSS), we develop sampling strategies that can reduce cost and increase efficiency of the regression analysis for the above-mentioned situation. The developed method is applied retrospectively to a lung cancer study. In the lung cancer study, the interest is to investigate the association between smoking status and three biomarkers: polyphenol DNA adducts, micronuclei, and sister chromatic exchanges. Optimal sampling schemes with different optimality criteria such as A-, D-, and integrated mean square error (IMSE)-optimality are considered in the application. With set size 10 in RSS, the improvement of the optimal schemes over simple random sampling (SRS) is great. For instance, by using the optimal scheme with IMSE-optimality, the IMSEs of the estimated regression functions for the three biomarkers are reduced to about half of those incurred by using SRS.

Regression Rate Studies in Hypergolic System

Regression ra tes of a hypergolic combination of fuel and oxidiser have been experimentally measured as a function of chamber pressure, mass flux and the percentage component of the hypergolic compound in natural rubber. The hypergolic compound used is difurfurylidene cyclohexanone (DFCH) which is hypergolic with the oxidiser red fuming nitric acid (RFNA) with ignition dela y of 60-70 ms. The data of weight loss versus time is obtained for burn times varying between 5 and 20 seconds. Two methods of correlating the data using mass flux of oxidiser and the total flux of hot gases have shown that index n of the regression law r=aGoxn or r=aGnxn-1 (x the axial distance) is about 0.5 or a little lower and not 0.8 even though the flow through the port is turbulent. It is argued that the reduction of index n is due to heterogeneous reaction between the liquid oxidiser and the hypergolic fuel component on the surface.

Joint Regression and Association Models for Repeated Categorical Responses

The focus of this study is on statistical analysis of categorical responses, where the response values are dependent of each other. The most typical example of this kind of dependence is when repeated responses have been obtained from the same study unit. For example, in Paper I, the response of interest is the pneumococcal nasopharengyal carriage (yes/no) on 329 children. For each child, the carriage is measured nine times during the first 18 months of life, and thus repeated respones on each child cannot be assumed independent of each other. In the case of the above example, the interest typically lies in the carriage prevalence, and whether different risk factors affect the prevalence. Regression analysis is the established method for studying the effects of risk factors. In order to make correct inferences from the regression model, the associations between repeated responses need to be taken into account. The analysis of repeated categorical responses typically focus on regression modelling. However, further insights can also be gained by investigating the structure of the association. The central theme in this study is on the development of joint regression and association models. The analysis of repeated, or otherwise clustered, categorical responses is computationally difficult. Likelihood-based inference is often feasible only when the number of repeated responses for each study unit is small. In Paper IV, an algorithm is presented, which substantially facilitates maximum likelihood fitting, especially when the number of repeated responses increase. In addition, a notable result arising from this work is the freely available software for likelihood-based estimation of clustered categorical responses.

Deformation and stability regression models for soil nail walls

Lateral displacement and global stability are the two main stability criteria for soil nail walls. Conventional design methods do not adequately address the deformation behaviour of soil nail walls, owing to the complexity involved in handling a large number of influencing factors. Consequently, limited methods of deformation estimates based on empirical relationships and in situ performance monitoring are available in the literature. It is therefore desirable that numerical techniques and statistical methods are used in order to gain a better insight into the deformation behaviour of soil nail walls. In the present study numerical experiments are conducted using a 2 4 factorial design method. Based on analysis of the maximum lateral deformation and factor-of-safety observations from the numerical experiments, regression models for maximum lateral deformation and factor-of-safety prediction are developed and checked for adequacy. Selection of suitable design factors for the 2 4 factorial design of numerical experiments enabled the use of the proposed regression models over a practical range of soil nail wall heights and in situ soil variability. It is evident from the model adequacy analyses and illustrative example that the proposed regression models provided a reasonably good estimate of the lateral deformation and global factor of safety of the soil nail walls.

Land use regression model (LUR) for ultrafine particles in Brisbane

Traffic-related air pollution has been associated with a wide range of adverse health effects. One component of traffic emissions that has been receiving increasing attention is ultrafine particles(UFP, < 100 nm), which are of concern to human health due to their small diameters. Vehicles are the dominant source of UFP in urban environments. Small-scale variation in ultrafine particle number concentration (PNC) can be attributed to local changes in land use and road abundance. UFPs are also formed as a result of particle formation events. Modelling the spatial patterns in PNC is integral to understanding human UFP exposure and also provides insight into particle formation mechanisms that contribute to air pollution in urban environments. Land-use regression (LUR) is a technique that can use to improve the prediction of air pollution.

Changes in testicular function following specific deprivation of LH in the adult male rabbit

Sexually mature male rabbits actively immunized against highly purified ovine LH (oLH) were used as a model system to study the effects of endogenous LH deprivation (and therefore testosterone) on spermatogenesis as well as pituitary FSH secretion. Immunization against oLH generated antibody titres capable of cross-reacting and neutralizing rabbit LH and this resulted in a significant reduction (P<0.01) in serum testosterone levels by 2-4 weeks of immunization. A significant increase in circulating FSH concentration (from a basal level of similar to 1 ng to 60-100 ng/ml; P<0.01) was observed within 4-6 weeks of immunization, perhaps a consequence of the negative feedback effect of the lack of testosterone. The effect of LH deprivation on spermatogenesis assessed by DNA flow cytometry and histological analyses of testicular biopsy tissue revealed that lack of testosterone primarily results in a rapid reduction and complete absence of round (1C) and elongated (HC) spermatids. The immediate effect of LH/testosterone deprivation thus appears to be at the step of meiotic transformation of primary spermatocytes (4C) to 1C. A significant reduction (>80%; P<0.01) in the 4C population and a relative accumulation (>90%; P<0.01) in spermatogonia (2C) was also observed, suggesting a need for testosterone during the transformation of 2C to 1C. In all but one of the rabbits, both qualitative and quantitative recovery in spermatogenesis occurred during the recovery phase, even at a time when only a marginal increase in serum testosterone (compared with the preimmunization) levels was observed as a result of a rapid decline in the cross-reactive antibody titres. These results clearly show that LH/testosterone deprivation in addition to primarily affecting the meiotic step also regulates the conversion of 2C to 4C during spermatogenesis.

Testicular function in adolescent boys with Klinefelter syndrome

Klinefelter syndrome (KS) is the most frequent karyotype disorder of male reproductive function. Since its original clinical description in 1942 and the identification of its chromosomal basis 47,XXY in 1959, the typical KS phenotype has become well recognized, but the mechanisms behind the testicular degeneration process have remained unrevealed. This prospective study was undertaken to increase knowledge about testicular function in adolescent KS boys. It comprised a longitudinal follow-up of growth, pubertal development, and serum reproductive hormone levels in 14 prepubertal and pubertal KS boys. Each boy had a testicular biopsy that was analyzed with histomorphometric and immunohistochemical methods. The KS boys had sufficient testosterone levels to allow normal onset and progression of puberty. Their serum testosterone levels remained within the low-normal range throughout puberty, but from midpuberty onwards, findings like a leveling-off in testosterone and insulin-like factor 3 (INSL3) concentrations, high gonadotropin levels, and exaggerated responses to gonadotropin-releasing hormone stimulation suggest diminished testosterone secretion. We also showed that the Leydig cell differentiation marker INSL3 may serve as a novel marker for onset and normal progression of puberty in boys. In the KS boys the number of germ cells was already markedly lower at the onset of puberty. The pubertal activation of the pituitary-testicular axis accelerated germ cell depletion, and germ cell differentiation was at least partly blocked at the spermatogonium or early primary spermatocyte stages. The presence of germ cells correlated with serum reproductive hormone levels. The immature Sertoli cells were incapable of transforming to the adult type, and during puberty the degeneration of Sertoli cells increased markedly. The older KS boys displayed an evident Leydig cell hyperplasia, as well as fibrosis and hyalinization of the interstitium and peritubular connective tissue. Altered immunoexpression of the androgen receptor (AR) suggested that in KS boys during puberty a relative androgen deficiency develops at testicular level. The impact of genetic features of the supernumerary X chromosome on the KS phenotype was also studied. The present study suggests that parental origin of the supernumerary X chromosome and the length of the CAG repeat of the AR gene influence pubertal development and testicular degeneration. The current study characterized by several means the testicular degeneration process in the testes of adolescent KS boys and confirmed that this process accelerates at the onset of puberty. Although serum reproductive hormone levels indicated no hypogonadism during early puberty, the histological analyses showed an already markedly reduced fertility potential in prepubertal KS boys. Genetic features of the X chromosome affect the KS phenotype.

A fast dual algorithm for kernel logistic regression

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.

Generalised regression estimation for domain class frequencies

This study examines the properties of Generalised Regression (GREG) estimators for domain class frequencies and proportions. The family of GREG estimators forms the class of design-based model-assisted estimators. All GREG estimators utilise auxiliary information via modelling. The classic GREG estimator with a linear fixed effects assisting model (GREG-lin) is one example. But when estimating class frequencies, the study variable is binary or polytomous. Therefore logistic-type assisting models (e.g. logistic or probit model) should be preferred over the linear one. However, other GREG estimators than GREG-lin are rarely used, and knowledge about their properties is limited. This study examines the properties of L-GREG estimators, which are GREG estimators with fixed-effects logistic-type models. Three research questions are addressed. First, I study whether and when L-GREG estimators are more accurate than GREG-lin. Theoretical results and Monte Carlo experiments which cover both equal and unequal probability sampling designs and a wide variety of model formulations show that in standard situations, the difference between L-GREG and GREG-lin is small. But in the case of a strong assisting model, two interesting situations arise: if the domain sample size is reasonably large, L-GREG is more accurate than GREG-lin, and if the domain sample size is very small, estimation of assisting model parameters may be inaccurate, resulting in bias for L-GREG. Second, I study variance estimation for the L-GREG estimators. The standard variance estimator (S) for all GREG estimators resembles the Sen-Yates-Grundy variance estimator, but it is a double sum of prediction errors, not of the observed values of the study variable. Monte Carlo experiments show that S underestimates the variance of L-GREG especially if the domain sample size is minor, or if the assisting model is strong. Third, since the standard variance estimator S often fails for the L-GREG estimators, I propose a new augmented variance estimator (A). The difference between S and the new estimator A is that the latter takes into account the difference between the sample fit model and the census fit model. In Monte Carlo experiments, the new estimator A outperformed the standard estimator S in terms of bias, root mean square error and coverage rate. Thus the new estimator provides a good alternative to the standard estimator.

Minimum enclosing spheres formulations for support vector ordinal regression

We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. Both approaches guarantee that the radii of the spheres are properly ordered at the optimal solution. The size of the optimization problem is linear in the number of training samples. The popular SMO algorithm is adapted to solve the resulting optimization problem. Numerical experiments on some real-world data sets verify the usefulness of our approaches for data mining.

Construction and use of linear regression models for processor performance analysis

Processor architects have a challenging task of evaluating a large design space consisting of several interacting parameters and optimizations. In order to assist architects in making crucial design decisions, we build linear regression models that relate Processor performance to micro-architecture parameters, using simulation based experiments. We obtain good approximate models using an iterative process in which Akaike's information criteria is used to extract a good linear model from a small set of simulations, and limited further simulation is guided by the model using D-optimal experimental designs. The iterative process is repeated until desired error bounds are achieved. We used this procedure to establish the relationship of the CPI performance response to 26 key micro-architectural parameters using a detailed cycle-by-cycle superscalar processor simulator The resulting models provide a significance ordering on all micro-architectural parameters and their interactions, and explain the performance variations of micro-architectural techniques.