Estimating Familial Correlations Using a Kotz Type Density

Two useful familial correlations often used to study the resemblance between the family members are the sib-sib correlation (ρss) and the mom-sib or parent-sib correlation (ρps). Since their introduction early in the last century by Galton, Fisher and others, many improved estimators of these correlations have been suggested in the literature. Several moment based estimators as well as the maximum likelihood estimators under the assumption of multivariate normality have been extensively studied and compared by various authors. However, the performance of these estimators when the data are not from multivariate normal distribution is poor. In this dissertation, we provide alternative estimates of ρss and ρps by minimizing the objective function,[special characters omitted]where Σ is a positive definite matrix with an appropriate structure involving ρss and ρps. Using extensive simulations from different multivariate distributions and using the bias, the mean squared error, and Pitman probability of nearness we have established that the alternative estimators are better than the existing estimators in most situations. The problems of testing of hypothesis about ρss and ρps and those of testing the equality of two sib-sib correlations and two mom-sib correlations are also considered. Alternative tests using Srivastava\u27s well known estimators of sib-sib and mom-sib correlations and their asymptotic variances are proposed and compared using simulations. The proposed tests have better estimated sizes and powers than the likelihood based tests when data are from a multivariate normal distribution. Proposed methods are illustrated on Galton\u27s famous classical data set on statures of families. These data are important, in that, the original note book on which these data were recorded by Galton in 1886 has been recently discovered and digitalized

Conjugate Bayesian analysis of compound-symmetric Gaussian models

We discuss Bayesian inference for a known-mean Gaussian model with a compound symmetric variance-covariance matrix. Since the space of such matrices is a linear subspace of that of positive definite matrices, we utilize the methods of Pisano (2022) to decompose the usual Wishart conjugate prior and derive a closed-form, three-parameter, bivariate conjugate prior distribution for the compound-symmetric half-precision matrix. The off-diagonal entry is found to have a non-central Kummer-Beta distribution conditioned on the diagonal, which is shown to have a gamma distribution generalized with Gauss's hypergeometric function. Such considerations yield a treatment of maximum a posteriori estimation for such matrices in Gaussian settings, including the Bayesian evidence and flexibility penalty attributable to Rougier and Priebe (2019). We also demonstrate how the prior may be utilized to naturally test for the positivity of a common within-class correlation in a random-intercept model using two data-driven examples

GAUDIE: Development, validation, and exploration of a naturalistic German AUDItory Emotional database

Since thoroughly validated naturalistic affective German speech stimulus databases are rare, we present here a novel validated database of speech sequences assembled with the purpose of emotion induction. The database comprises 37 audio speech sequences with a total duration of 92 minutes for the induction of positive, neutral, and negative emotion: comedian shows intending to elicit humorous and amusing feelings, weather forecasts, and arguments between couples and relatives from movies or television series. Multiple continuous and discrete ratings are used to validate the database to capture the time course and variabilities of valence and arousal. We analyse and quantify how well the audio sequences fulfil quality criteria of differentiation, salience/strength, and generalizability across participants. Hence, we provide a validated speech database of naturalistic scenarios suitable to investigate emotion processing and its time course with German-speaking participants. Information on using the stimulus database for research purposes can be found at the OSF project repository GAUDIE: https://osf.io/xyr6j/

Modeling correlation in binary count data with application to fragile site identification

Available fragile site identification software packages (FSM and FSM3) assume that all chromosomal breaks occur independently. However, under a Mendelian model of inheritance, homozygosity at fragile loci implies pairwise correlation between homologous sites. We construct correlation models for chromosomal breakage data in situations where either partitioned break count totals (per-site single-break and doublebreak totals) are known or only overall break count totals are known. We derive a likelihood ratio test and NeymanâÂÂs C( ñ) test for correlation between homologs when partitioned break count totals are known and outline a likelihood ratio test for correlation using only break count totals. Our simulation studies indicate that the C( ñ) test using partitioned break count totals outperforms the other two tests for correlation in terms of both power and level. These studies further suggest that the power for detecting correlation is low when only break count totals are reported. Results of the C( ñ) test for correlation applied to chromosomal breakage data from 14 human subjects indicate that detection of correlation between homologous fragile sites is problematic due to sparseness of breakage data. Simulation studies of the FSM and FSM3 algorithms using parameter values typical for fragile site data demonstrate that neither algorithm is significantly affected by fragile site correlation. Comparison of simulated fragile site misclassification rates in the presence of zero-breakage data supports previous studies (Olmsted 1999) that suggested FSM has lower false-negative rates and FSM3 has lower false-positive rates

Bayesian analysis for the intraclass model and for the quantile semiparametric mixed-effects double regression models

This dissertation consists of three distinct but related research projects. The first two projects focus on objective Bayesian hypothesis testing and estimation for the intraclass correlation coefficient in linear models. The third project deals with Bayesian quantile inference for the semiparametric mixed-effects double regression models. In the first project, we derive the Bayes factors based on the divergence-based priors for testing the intraclass correlation coefficient (ICC). The hypothesis testing of the ICC is used to test the uncorrelatedness in multilevel modeling, and it has not well been studied from an objective Bayesian perspective. Simulation results show that the two sorts of Bayes factors have good performance in the hypothesis testing. Moreover, the Bayes factors can be easily implemented due to their unidimensional integral expressions. In the second project, we consider objective Bayesian analysis for the ICC in the context of normal linear regression model. We first derive two objective priors for the unknown parameters and show that both result in proper posterior distributions. Within a Bayesian decision-theoretic framework, we then propose an objective Bayesian solution to the problems of hypothesis testing and point estimation of the ICC based on a combined use of the intrinsic discrepancy loss function and objective priors. The proposed solution has an appealing invariance property under one-to-one reparameterization of the quantity of interest. Simulation studies are conducted to investigate the performance the proposed solution. Finally, a real data application is provided for illustrative purposes. In the third project, we study Bayesian quantile regression for semiparametric mixed effects model, which includes both linear and nonlinear parts. We adopt the popular cubic spline functions for the nonlinear part and model the variance of the random effect as a function of the explanatory variables. An efficient Gibbs sampler with the Metropolis-Hastings algorithm is proposed to generate posterior samples of the unknown parameters from their posterior distributions. Simulation studies and a real data example are used to illustrate the performance of the proposed methodology

Standardization of the antibody-dependent respiratory burst assay with human neutrophils and Plasmodium falciparum malaria.

The assessment of naturally-acquired and vaccine-induced immunity to blood-stage Plasmodium falciparum malaria is of long-standing interest. However, the field has suffered from a paucity of in vitro assays that reproducibly measure the anti-parasitic activity induced by antibodies in conjunction with immune cells. Here we optimize the antibody-dependent respiratory burst (ADRB) assay, which assesses the ability of antibodies to activate the release of reactive oxygen species from human neutrophils in response to P. falciparum blood-stage parasites. We focus particularly on assay parameters affecting serum preparation and concentration, and importantly assess reproducibility. Our standardized protocol involves testing each serum sample in singlicate with three independent neutrophil donors, and indexing responses against a standard positive control of pooled hyper-immune Kenyan sera. The protocol can be used to quickly screen large cohorts of samples from individuals enrolled in immuno-epidemiological studies or clinical vaccine trials, and requires only 6 μL of serum per sample. Using a cohort of 86 samples, we show that malaria-exposed individuals induce higher ADRB activity than malaria-naïve individuals. The development of the ADRB assay complements the use of cell-independent assays in blood-stage malaria, such as the assay of growth inhibitory activity, and provides an important standardized cell-based assay in the field

Development and Application of a Nostalgia Scale for Sport Tourism: A Multilevel Approach

Nostalgia is best described as a sentimental and bittersweet yearning for a positive and pleasant past, particularly when juxtaposed with an unsatisfying present and uncertain future. One\u27s positive memories surely influence the evocation of nostalgia, and an individual\u27s negative feelings for the present or future are also related to nostalgia, since a person cannot return to the past. In other words, both positive and negative feelings are associated with nostalgia, and it is called a bittersweet emotion. In many cases, people are influenced by their past memories when they decide to attend sports events. As a result, individuals have their own attitudes based on their past memories, and it may affect individual\u27s behavioral intentions. The NCAA football game is one of the most popular and historic sporting events in United States. According to U. S. Census Bureau (n.d.), the NCAA College football drew the second largest number of spectators in the United States from 1990 to 2010. The population of this study is people who attended Clemson football games. Specifically, this study surveyed participants who have had a positive past experience at the Clemson football home games, and a systematic sampling technique was used for gathering the data. The goal of this study was to develop a comprehensive conceptual framework of nostalgia in the context of sport tourism and to provide a valid and reliable nostalgia scale for sport tourism (NSST) based on a suggested classification of nostalgia in sport tourism. Another aim of this study was to verify the developed nostalgia scale for sport tourism by testing the relationship among nostalgia (independent variable), attitude (mediating variable), and behavioral intentions (dependent variable). To clarify the group effects, this study uses multilevel structural equation modeling. The results of this study indicated that attitude mediates the relationship between nostalgia and behavioral intentions in multilevel structural equation model. In addition, this study discusses how nostalgia plays a role in sport tourism and suggests the direction for sustainable development of nostalgia sport tourism

Modeling correlation in binary count data with application to fragile site identification

Available fragile site identification software packages (FSM and FSM3) assume that all chromosomal breaks occur independently. However, under a Mendelian model of inheritance, homozygosity at fragile loci implies pairwise correlation between homologous sites. We construct correlation models for chromosomal breakage data in situations where either partitioned break count totals (per-site single-break and doublebreak totals) are known or only overall break count totals are known. We derive a likelihood ratio test and NeymanâÂÂs C( ñ) test for correlation between homologs when partitioned break count totals are known and outline a likelihood ratio test for correlation using only break count totals. Our simulation studies indicate that the C( ñ) test using partitioned break count totals outperforms the other two tests for correlation in terms of both power and level. These studies further suggest that the power for detecting correlation is low when only break count totals are reported. Results of the C( ñ) test for correlation applied to chromosomal breakage data from 14 human subjects indicate that detection of correlation between homologous fragile sites is problematic due to sparseness of breakage data. Simulation studies of the FSM and FSM3 algorithms using parameter values typical for fragile site data demonstrate that neither algorithm is significantly affected by fragile site correlation. Comparison of simulated fragile site misclassification rates in the presence of zero-breakage data supports previous studies (Olmsted 1999) that suggested FSM has lower false-negative rates and FSM3 has lower false-positive rates

Machine learning techniques for high dimensional data

This thesis presents data processing techniques for three different but related application areas: embedding learning for classification, fusion of low bit depth images and 3D reconstruction from 2D images. For embedding learning for classification, a novel manifold embedding method is proposed for the automated processing of large, varied data sets. The method is based on binary classification, where the embeddings are constructed so as to determine one or more unique features for each class individually from a given dataset. The proposed method is applied to examples of multiclass classification that are relevant for large scale data processing for surveillance (e.g. face recognition), where the aim is to augment decision making by reducing extremely large sets of data to a manageable level before displaying the selected subset of data to a human operator. In addition, an indicator for a weighted pairwise constraint is proposed to balance the contributions from different classes to the final optimisation, in order to better control the relative positions between the important data samples from either the same class (intraclass) or different classes (interclass). The effectiveness of the proposed method is evaluated through comparison with seven existing techniques for embedding learning, using four established databases of faces, consisting of various poses, lighting conditions and facial expressions, as well as two standard text datasets. The proposed method performs better than these existing techniques, especially for cases with small sets of training data samples. For fusion of low bit depth images, using low bit depth images instead of full images offers a number of advantages for aerial imaging with UAVs, where there is a limited transmission rate/bandwidth. For example, reducing the need for data transmission, removing superfluous details, and reducing computational loading of on-board platforms (especially for small or micro-scale UAVs). The main drawback of using low bit depth imagery is discarding image details of the scene. Fortunately, this can be reconstructed by fusing a sequence of related low bit depth images, which have been properly aligned. To reduce computational complexity and obtain a less distorted result, a similarity transformation is used to approximate the geometric alignment between two images of the same scene. The transformation is estimated using a phase correlation technique. It is shown that that the phase correlation method is capable of registering low bit depth images, without any modi�cation, or any pre and/or post-processing. For 3D reconstruction from 2D images, a method is proposed to deal with the dense reconstruction after a sparse reconstruction (i.e. a sparse 3D point cloud) has been created employing the structure from motion technique. Instead of generating a dense 3D point cloud, this proposed method forms a triangle by three points in the sparse point cloud, and then maps the corresponding components in the 2D images back to the point cloud. Compared to the existing methods that use a similar approach, this method reduces the computational cost. Instated of utilising every triangle in the 3D space to do the mapping from 2D to 3D, it uses a large triangle to replace a number of small triangles for flat and almost flat areas. Compared to the reconstruction result obtained by existing techniques that aim to generate a dense point cloud, the proposed method can achieve a better result while the computational cost is comparable