零售生存数据中的偏好抽样：东京小型超市关闭的贝叶斯联合LGCP和空间Probit模型
Assessing Preferential Sampling in Retail Survival Data: A Bayesian Joint LGCP and Spatial Probit Model for Mini-Supermarket Closure in Tokyo
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中文总结 AI 辅助
研究零售生存数据中因位置因素致偏好抽样问题，提出贝叶斯联合LGCP和空间Probit模型，用特定算法和协变量等进行分析，结果显示能区分偏好抽样，且靠近大型超市是关闭风险的稳健预测因素。
中文摘要 AI 辅助
零售商店位置是经过策略性选择而非随机分布的，当控制位置的潜在空间因素也影响商店生存时，可能会导致偏好抽样。我们提出了一种贝叶斯分层模型，将用于商店位置的对数高斯Cox过程与用于二元生存结果的Probit回归联合起来。两个组件共享高斯过程空间效应，通过一个负荷参数衡量商店位置和生存的潜在驱动因素之间的关联。为了对约1000个观测值进行有效推断，我们使用最近邻高斯过程近似和Gibbs内Metropolis算法。我们将该模型应用于东京23个特别区的999家小型超市，包括897家营业和102家关闭的商店，使用七个空间协变量和一个3471点的积分网格。估计的负荷接近零，其可信区间包含零，没有明确的残余偏好抽样证据。回归估计在有和没有偏好抽样的模型中也很稳定。模拟表明该方法可以区分不存在和强烈的偏好抽样。靠近大型超市是关闭风险最稳健的预测因素，与竞争替代一致。
英文摘要
Retail store locations are strategically selected rather than randomly distributed, potentially inducing preferential sampling when the latent spatial factors governing placement also affect store survival. We propose a Bayesian hierarchical model that jointly combines a log-Gaussian Cox process for store locations with a probit regression for binary survival outcomes. The two components share a Gaussian process spatial effect, with a loading parameter measuring the association between the latent drivers of store placement and survival. To enable efficient inference for approximately 1,000 observations, we use a nearest-neighbor Gaussian process approximation and a Metropolis-within-Gibbs algorithm. We apply the model to 999 mini-supermarkets in Tokyo's 23 special wards, including 897 operating and 102 closed stores, using seven spatial covariates and a 3,471-point integration grid. The estimated loading is close to zero, with its credible interval including zero, providing no clear evidence of residual preferential sampling. Regression estimates are also stable across models with and without preferential sampling. Simulations show that the method can distinguish absent from strong preferential sampling. Proximity to full-scale supermarkets is the most robust predictor of closure risk, consistent with competitive substitution.
