Topics In Forward Stepwise Logistic Regression

In this dissertation, five topics related to the process and prediction of forward stepwise logistic regression are investigated.;Forward stepwise logistic regression is involved with selection and stopping criteria. Seven selection criteria are used: the likelihood ratio statistic, Lawless and Singhal (1978)\u27s statistic, the Wald statistic, the score statistic, Peduzzi, Hardy, and Holford (1980)\u27s statistic, Lee and Koval\u27s statistic (LK), and a sweep operator\u27s statistic (SW). Five stopping criteria are used: {dollar}\chi\sp2{dollar} test based on a fixed {dollar}\alpha{dollar} level, minimum value of ERR, minimum value of the C{dollar}\sb{lcub}\rm p{rcub}{dollar} statistic (Hosmer, 1989), minimum value of the Akaike information criterion (Akaike, 1974), and minimum value of Schwarz\u27s criterion (Schwarz, 1978).;Apparent error tate (ARR) tends to underestimate true error rate (ERR). In our study, estimated true error rate (ERR) is obtained by ERR = ARR + {dollar}\\omega{dollar}, where {dollar}\\omega{dollar} is from Efron (1986)\u27s parametric estimate of bias for ARR.;We use Monte Carlo simulation with both multivariate normal and multivariate binary independent variables; we implement the simulation with SAS/IML programs. We then analyze the experimental design to see which factors of the distribution of independent variables affect various outcomes.;As a result, we recommend the best {dollar}\alpha{dollar} level for the {dollar}\chi\sbsp{lcub}(\alpha){rcub}{lcub}2{rcub}{dollar} stopping criterion. Second, we compare the order of variables selected by different selection criteria. Third, we investigate the effects of different structures of predictor variables on ARR, {dollar}\\omega{dollar}, and ERR. Fourth, we compare the sizes of subset models determined by different stopping criteria. Finally, we compare the performances of selection and stopping criteria in terms of ERR

Improving Term Frequency Normalization for Multi-topical Documents, and Application to Language Modeling Approaches

Term frequency normalization is a serious issue since lengths of documents are various. Generally, documents become long due to two different reasons - verbosity and multi-topicality. First, verbosity means that the same topic is repeatedly mentioned by terms related to the topic, so that term frequency is more increased than the well-summarized one. Second, multi-topicality indicates that a document has a broad discussion of multi-topics, rather than single topic. Although these document characteristics should be differently handled, all previous methods of term frequency normalization have ignored these differences and have used a simplified length-driven approach which decreases the term frequency by only the length of a document, causing an unreasonable penalization. To attack this problem, we propose a novel TF normalization method which is a type of partially-axiomatic approach. We first formulate two formal constraints that the retrieval model should satisfy for documents having verbose and multi-topicality characteristic, respectively. Then, we modify language modeling approaches to better satisfy these two constraints, and derive novel smoothing methods. Experimental results show that the proposed method increases significantly the precision for keyword queries, and substantially improves MAP (Mean Average Precision) for verbose queries.Comment: 8 pages, conference paper, published in ECIR '0

ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothes

We present ClothCombo, a pipeline to drape arbitrary combinations of clothes on 3D human models with varying body shapes and poses. While existing learning-based approaches for draping clothes have shown promising results, multi-layered clothing remains challenging as it is non-trivial to model inter-cloth interaction. To this end, our method utilizes a GNN-based network to efficiently model the interaction between clothes in different layers, thus enabling multi-layered clothing. Specifically, we first create feature embedding for each cloth using a topology-agnostic network. Then, the draping network deforms all clothes to fit the target body shape and pose without considering inter-cloth interaction. Lastly, the untangling network predicts the per-vertex displacements in a way that resolves interpenetration between clothes. In experiments, the proposed model demonstrates strong performance in complex multi-layered scenarios. Being agnostic to cloth topology, our method can be readily used for layered virtual try-on of real clothes in diverse poses and combinations of clothes

A gap between hyponormality and subnormality for block Toeplitz operators

AbstractThis paper concerns a gap between hyponormality and subnormality for block Toeplitz operators. We show that there is no gap between 2-hyponormality and subnormality for a certain class of trigonometric block Toeplitz operators (e.g., its co-analytic outer coefficient is invertible). In addition we consider the extremal cases for the hyponormality of trigonometric block Toeplitz operators: in this case, hyponormality and normality coincide

Design and Development of a Run-Time Monitor for Multi-Core Architectures in Cloud Computing

Cloud computing is a new information technology trend that moves computing and data away from desktops and portable PCs into large data centers. The basic principle of cloud computing is to deliver applications as services over the Internet as well as infrastructure. A cloud is a type of parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources. The large-scale distributed applications on a cloud require adaptive service-based software, which has the capability of monitoring system status changes, analyzing the monitored information, and adapting its service configuration while considering tradeoffs among multiple QoS features simultaneously. In this paper, we design and develop a Run-Time Monitor (RTM) which is a system software to monitor the application behavior at run-time, analyze the collected information, and optimize cloud computing resources for multi-core architectures. RTM monitors application software through library instrumentation as well as underlying hardware through a performance counter optimizing its computing configuration based on the analyzed data

Structure of AGCM-Simulated Convectively Coupled Kelvin Waves and Sensitivity to Convective Parameterization

A study of the convectively coupled Kelvin wave (CCKW) properties from a series of atmospheric general circulation model experiments over observed sea surface temperatures is presented. The simulations are performed with two different convection schemes (a mass flux scheme and a moisture convergence scheme) using a range of convective triggers, which inhibit convection in different ways. Increasing the strength of the convective trigger leads to significantly slower and more intense CCKW activity in both convection schemes. With the most stringent trigger in the mass flux scheme, the waves have realistic speed and variance and also exhibit clear shallow-to-deep-to-stratiform phase tilts in the vertical, as in observations. While adding a moisture trigger results in vertical phase tilts in the mass flux scheme, the moisture convergence scheme CCKWs show no such phase tilts even with a stringent convective trigger. The changes in phase speed in the simulations are interpreted using the concept of "gross moist stability" (GMS). Inhibition of convection results in a more unstable tropical atmosphere in the time mean, and convection is shallower on average as well. Both of these effects lead to a smaller GMS, which leads to slower propagation of the waves, as expected from theoretical studies. Effects such as changes in radiative heating, atmospheric humidity, and vertical velocity following the wave have a relatively small effect on the GMS as compared with the time mean state determined by the convection scheme.open222