Comparison of three expert elicitation methods for logistic regression on predicting the presence of the threatened brush-tailed rock-wallaby petrogale penicillata

Numerous expert elicitation methods have been suggested for generalised linear models (GLMs). This paper compares three relatively new approaches to eliciting expert knowledge in a form suitable for Bayesian logistic regression. These methods were trialled on two experts in order to model the habitat suitability of the threatened Australian brush-tailed rock-wallaby (Petrogale penicillata). The first elicitation approach is a geographically assisted indirect predictive method with a geographic information system (GIS) interface. The second approach is a predictive indirect method which uses an interactive graphical tool. The third method uses a questionnaire to elicit expert knowledge directly about the impact of a habitat variable on the response. Two variables (slope and aspect) are used to examine prior and posterior distributions of the three methods. The results indicate that there are some similarities and dissimilarities between the expert informed priors of the two experts formulated from the different approaches. The choice of elicitation method depends on the statistical knowledge of the expert, their mapping skills, time constraints, accessibility to experts and funding available. This trial reveals that expert knowledge can be important when modelling rare event data, such as threatened species, because experts can provide additional information that may not be represented in the dataset. However care must be taken with the way in which this information is elicited and formulated.

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

A hybrid approach to combining CART and logistic regression for stock ranking

The benefits of applying tree-based methods to the purpose of modelling financial assets as opposed to linear factor analysis are increasingly being understood by market practitioners. Tree-based models such as CART (classification and regression trees) are particularly well suited to analysing stock market data which is noisy and often contains non-linear relationships and high-order interactions. CART was originally developed in the 1980s by medical researchers disheartened by the stringent assumptions applied by traditional regression analysis (Brieman et al. [1984]). In the intervening years, CART has been successfully applied to many areas of finance such as the classification of financial distress of firms (see Frydman, Altman and Kao [1985]), asset allocation (see Sorensen, Mezrich and Miller [1996]), equity style timing (see Kao and Shumaker [1999]) and stock selection (see Sorensen, Miller and Ooi [2000])...

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.

On the bifurcation in a “modulated” logistic map

We comment on a paper by Luang [On the bifurcation in a ''modulated'' logistic map, Physics Letters A 194(1994) 57]. The numerical evidence given in that paper, for a peculiar type of bifurcation, is shown to be incorrect. The causes of such anomalous results are explained. An accurate bifurcation diagram for the map is also given.

Learning polyhedral classifiers using logistic function

In this paper we propose a new algorithm for learning polyhedral classifiers. In contrast to existing methods for learning polyhedral classifier which solve a constrained optimization problem, our method solves an unconstrained optimization problem. Our method is based on a logistic function based model for the posterior probability function. We propose an alternating optimization algorithm, namely, SPLA1 (Single Polyhedral Learning Algorithm1) which maximizes the loglikelihood of the training data to learn the parameters. We also extend our method to make it independent of any user specified parameter (e.g., number of hyperplanes required to form a polyhedral set) in SPLA2. We show the effectiveness of our approach with experiments on various synthetic and real world datasets and compare our approach with a standard decision tree method (OC1) and a constrained optimization based method for learning polyhedral sets.

Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression

Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved using dual coordinate descent methods developed for non-sparse L2-regularized linear SVMs and LR, without altering their iteration complexity and convergence properties. Experiments on very large datasets indicate that the proposed dual coordinate descent - projection (DCD-P) methods are fast and achieve comparable generalization performance after the first pass through the data, with extremely sparse models.

Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.

Synchronous chaos in the coupled system of two logistic maps

Synchronous chaos is investigated in the coupled system of two Logistic maps. Although the diffusive coupling admits all synchronized motions, the stabilities of their configurations are dependent on the transverse Lyapunov exponents while independent of the longitudinal Lyapunov exponents. It is shown that synchronous chaos is structurally stable with respect to the system parameters. The mean motion is the pseudo-orbit of an individual local map so that its dynamics can be described by the local map. (C) 2004 Elsevier Ltd. All rights reserved.

Using an inverse-logistic model to describe growth increments of blacklip abalone (Haliotis rubra) in Tasmania

A new description of growth in blacklip abalone (Haliotis rubra) with the use of an inverse-logistic model is introduced. The inverse-logistic model avoids the disadvantageous assumptions of either rapid or slow growth for small and juvenile individuals implied by the von Bertalanffy and Gompertz growth models, respectively, and allows for indeterminate growth where necessary. An inverse-logistic model was used to estimate the expected mean growth increment for different black-lip abalone populations around southern Tasmania, Australia. Estimates of the time needed for abalone to grow from settlement until recruitment (at 138 mm shell length) into the fishery varied from eight to nine years. The variability of the residuals about the predicted mean growth increments was described with either a second inverse-logistic relationship (standard deviation vs. initial length) or by a power relationship (standard deviation vs. predicted growth increment). The inverse-logistic model can describe linear growth of small and juvenile abalone (as observed in Tasmania), as well as a spectrum of growth possibilities, from determinate to indeterminate growth (a spectrum that would lead to a spread of maximum lengths).

Logistic回归分析在厚颌鲂生活史类型研究中的应用

以34种中国淡水鱼类和近海鱼类的生态参数为基础,建立logistic回归模型。利用该模型研究长江上游龙溪河厚颌鲂的生活史类型,然后用平衡产量模型进行验证,并探讨合理开发龙溪河厚颌鲂种群资源的渔业管理措施。结果表明:logistic回归模型估算出厚颌鲂属于k选择鱼类的概率为13%,判别标准为29%,因此厚颌鲂应该属于r选择鱼类;Beverton-Holt产量模型分析结果显示厚颌鲂平衡产量曲线与典型的r选择鱼类尖头塘鳢极为类似;捕捞死亡系数和起捕年龄的变化对产量曲线有显著影响,因此限制捕捞量和网目大小是控制过

Logistic Map耦合格子模型模拟环带

本论文采用Logistic Map耦合格子模型对高聚物中特有的环带球晶进行了模拟，所得到的模拟结果与实验结果吻合较好。同时，研究结果能够对实验制备环带球晶样品提供可靠的理论指导。 首先，我们对Logistic Map耦合格子模型及模型中的两个模拟参量μ和ε进行分析，同时结合实验中各种实验条件对聚合物结晶行为的影响，认为Logistic Map的动力学特征与聚合物结晶行为非常相似，并且参量μ与实验中的结晶温度相关，即随温度的升高而减小，而参量ε与实验中影响扩散的因素有关，即随温度的升高而增大、随分子量的增大而减小，并且随样品厚度的增大而增大。我们对模型的整个参数空间进行计算，得到了可以形成环带球晶形貌的参数范围，通过进一步研究发现环带图案的带宽随参量μ的增大而变窄，随参量ε的增大而变宽。上述研究结果与实验中带宽随实验条件的变化规律是一致的。 在得到环带图案的基础上，我们又进一步计算得到了靶状和螺旋状形貌的参量μ和ε的具体取值范围。通过改变μ和ε的参数取值，模拟了环带球晶形貌由靶状过渡到螺旋状的过程，即靶状图案的环带由外层向内层逐渐断裂成较短的条带结构，所有的条带结构呈现出以空间某处为中心团聚在一起的形貌；随后，这种“团聚”的形貌逐渐消失了，空间中小的条带结构的排列呈无序状态。随着参数的进一步变化，短的条带结构变成较长的带状结构，并且这些带状结构的边缘逐渐发生卷曲，最终形成了螺旋状图案。我们还考察了系统初值和耦合方式对上述图案的影响，结果发现，形成环带球晶的参数范围对系统初值没有明显的依赖性，然而靶状和螺旋状图案的分布受初值的影响较大。此外，发现只有采用交替耦合、并考虑长程耦合作用的Logistic Map耦合格子模型才可以得到环带球晶图案。 为了更好地与实验结果进行对比，我们利用Logistic Map耦合格子模型对二维空间中的几种受限体系进行了模拟。（一）对温度梯度场中的环带球晶进行模拟，发现环带球晶在低温处较易成核，向高温处生长，并且，高温处环带的带宽比低温处宽。（二）对格子宽度受限情况进行了模拟，发现随着受限方向的宽度越来越窄，球晶尺寸逐渐变小，相邻两个环带球晶碰撞产生的界线变短，三个相邻环带球晶所形成的界线的交汇点减少。（三）研究了受限边界上的成核作用对狭长格子中环带球晶的影响，结果发现，随着受限边界上成核点密度的不断增加，其形貌转变分为三个不同阶段：①当成核密度稍有增大时，环带球晶数量增加，直径变小；②继续增大边界成核密度，使得大量晶层从受限边界向格子内生长，导致环带球晶的数量减少，直径也减小；③当成核点增加到一定程度时，整个空间中只有极少数由格子内部成核生长且直径非常小的环带球晶，而占主导地位的是由成核点垂直于受限边界生长出的穿透晶层。这些模拟结果均与实验结果相符合。 我们将Logistic Map耦合映象格子模型发展到三维空间格子中，得到了与环带球晶形貌一致的图案，并且其带宽随模拟参量μ的增大而变窄，随ε的增大而变宽。这一规律性结果与二维正方格子的模拟结果是一致的。这一部分的研究结果还表明，边界条件和格子尺寸对模拟结果有显著的影响，周期性边界条件导致在小体积立方格子中只能得到靶状图案；而当格子尺寸很大时，可以得到螺旋状环带球晶的图案。最后，通过调节垂直于薄膜平面方向上的格子数来研究薄膜厚度对环带图案带宽的影响，发现环带的带宽随厚度的增加而变宽，这与实验中环带球晶的带宽随样品厚度的增加而变大的结论是一致的。