SOUTH AFRICAN STATISTICAL JOURNAL最新文献

An automated exact solution framework towards solving the logistic regression best subset selection problem 一个解决逻辑回归最佳子集选择问题的自动化精确解框架

SOUTH AFRICAN STATISTICAL JOURNAL Pub Date : 2023-01-01 DOI: 10.37920/sasj.2023.57.2.2

Thomas van Niekerk, Jacques V. Venter, Stephanus E. Terblanche

{"title":"An automated exact solution framework towards solving the logistic regression best subset selection problem","authors":"Thomas van Niekerk, Jacques V. Venter, Stephanus E. Terblanche","doi":"10.37920/sasj.2023.57.2.2","DOIUrl":"https://doi.org/10.37920/sasj.2023.57.2.2","url":null,"abstract":"An automated logistic regression solution framework (ALRSF) is proposed to solve a mixed integer programming (MIP) formulation of the well known logistic regression best subset selection problem. The solution framework firstly determines the optimal number of independent variables that should be included in the model using an automated cardinality parameter selection procedure. The cardinality parameter dictates the size of the subset of variables and can be problem-specific. A novel regression parameter fixing heuristic that utilises a Benders decomposition algorithm is applied to prune the solution search space such that the optimal regression parameter values are found faster. An optimality gap is subsequently calculated to quantify the quality of the final regression model by considering the distance between the best possible log-likelihood value and a log-likelihood value that is calculated using the current parameter values. Attempts are then made to reduce the optimality gap by adjusting regression parameter values. The ALRSF serves as a holistic variable selection framework that enables the user to consider larger datasets when solving the best subset selection logistic regression problem by significantly reducing the memory requirements associated with its mixed integer programming formulation. Furthermore, the automated framework requires minimal user intervention during model training and hyperparameter tuning. Improvements in quality of the final model (when considering both the optimality gap and computing resources required to achieve a result) are observed when the ALRSF is applied to well-known real-world UCI machine learning datasets. Keywords: Best subset selection, Independent variable selection, Logistic regression, Mixed integer programming","PeriodicalId":53997,"journal":{"name":"SOUTH AFRICAN STATISTICAL JOURNAL","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136258806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Covariate construction of nonconvex windows for spatial point patterns 空间点模式非凸窗口的协变量构造

SOUTH AFRICAN STATISTICAL JOURNAL Pub Date : 2023-01-01 DOI: 10.37920/sasj.2023.57.2.1

Kabelo Mahloromela, Inger Fabris-Rotelli, Christine Kraamwinkel

{"title":"Covariate construction of nonconvex windows for spatial point patterns","authors":"Kabelo Mahloromela, Inger Fabris-Rotelli, Christine Kraamwinkel","doi":"10.37920/sasj.2023.57.2.1","DOIUrl":"https://doi.org/10.37920/sasj.2023.57.2.1","url":null,"abstract":"In some standard applications of spatial point pattern analysis, window selection for spatial point pattern data is complex. Often, the point pattern window is given a priori. Otherwise, the region is chosen using some objective means reflecting a view that the window may be representative of a larger region. The typical approaches used are the smallest rectangular bounding window and convex windows. The chosen window should however cover the true domain of the point process since it defines the domain for point pattern analysis and supports estimation and inference. Choosing too large a window results in spurious estimation and inference in regions of the window where points cannot occur. We propose a new algorithm for selecting the point pattern domain based on spatial covariate information and without the restriction of convexity, allowing for better estimation of the true domain. Amodified kernel smoothed intensity estimate that uses the Euclidean shortest path distance is proposed as validation of the algorithm. The proposed algorithm is applied in the setting of rural villages in Tanzania. As a spatial covariate, remotely sensed elevation data is used. The algorithm is able to detect and filter out high relief areas and steep slopes; observed characteristics that make the occurrence of a household in these regions improbable. Keywords: Covariate, Euclidean shortest path, Nonconvex, Spatial point pattern, Window selection","PeriodicalId":53997,"journal":{"name":"SOUTH AFRICAN STATISTICAL JOURNAL","volume":"389 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136258807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the variance and skewness of the swap rate in a stochastic volatility interest rate model 随机波动率利率模型中掉期利率的方差和偏度

IF 0.3

SOUTH AFRICAN STATISTICAL JOURNAL Pub Date : 2021-09-01 DOI: 10.37920/sasj.2021.55.2.2

Lars Palapies

引用次数: 0

Time-variant nonparametric extreme quantile estimation with application to US temperature data 时变非参数极值分位数估计及其在美国气温数据中的应用

IF 0.3

SOUTH AFRICAN STATISTICAL JOURNAL Pub Date : 2021-09-01 DOI: 10.37920/sasj.2021.55.2.1

M. S. Chowdhury, Bogdan Gadidov, Linh Le, Yan Wang, L. Vanbrackle

引用次数: 0

Advantages of using factorisation machines as a statistical modelling technique 使用因子分解机器作为统计建模技术的优点

IF 0.3

SOUTH AFRICAN STATISTICAL JOURNAL Pub Date : 2021-09-01 DOI: 10.37920/sasj.2021.55.2.3

E. Slabber, T. Verster, Riaan de Jongh

引用次数: 0

Estimation of location parameter within pre-specified error bound with second-order efficient two-stage procedure 用二阶有效的两阶段方法估计预定误差范围内的位置参数

IF 0.3

SOUTH AFRICAN STATISTICAL JOURNAL Pub Date : 2021-03-31 DOI: 10.37920/SASJ.2021.55.1.4

Bhargab Chattopadhyay, Swarnali Banerjee

引用次数: 0

Contextual batting and bowling in limited overs cricket 上下文击球和保龄球在有限的板球

IF 0.3

SOUTH AFRICAN STATISTICAL JOURNAL Pub Date : 2021-03-31 DOI: 10.37920/SASJ.2021.55.1.6

James Thomson, Harsha Perera, T. Swartz

引用次数: 1

A generic test for the similarity of spatial data 空间数据相似性的通用检验

IF 0.3

SOUTH AFRICAN STATISTICAL JOURNAL Pub Date : 2021-03-31 DOI: 10.37920/SASJ.2021.55.1.5

R. Kirsten, I. Fabris-Rotelli

引用次数: 0

Regularisation in discrete survival models: A comparison of lasso and gradient boosting 离散生存模型中的正则化:套索和梯度提升的比较

IF 0.3

SOUTH AFRICAN STATISTICAL JOURNAL Pub Date : 2021-03-31 DOI: 10.37920/SASJ.2021.55.1.3

A. Bere, Godfrey H. Sithuba, Coster Mabvuu, Retang Mashabela, C. Sigauke, K. Kyei

引用次数: 0

Estimating the Gini index for heavy-tailed income distributions 重尾收入分配的基尼指数估计

IF 0.3

SOUTH AFRICAN STATISTICAL JOURNAL Pub Date : 2021-03-01 DOI: 10.37920/SASJ.2021.55.1.2

Amina Bari, A. Rassoul, Hamid Ould Rouis

引用次数: 0