Valid post-selection inference in model-free linear regression

By Arun K. Kuchibhotla, Lawrence D. Brown, Andreas Buja, Junhui Cai, Edward I. George, Linda H. Zhao. 2020

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S.1. Simulations Continued. The simulation setting in this section is the same as in Section 9. We first describe the reason for using the null situation β0 0p in the model. If β0 is an arbitrary non-zero vector, then, for fixed covariates, XiYi cannot be identically distributed and hence only (asymptotically) conservative inference is possible. In simulations this conservativeness confounds with the simultaneity so that the coverage becomes close to 1 (if not 1). In the main manuscript, we have shown plots comparing our method with Berk et al. (2013) and selective inference. We label our confidence region R̂:n,M (12) as “UPoSI,” the projected confidence region B̂ n,M (28) as “UPoSIBox”, and Berk et al. (2013) as “PoSI.” Tables 1, 2, and 3 show exact numbers for the comparison of our method with Berk et al. (2013). Note that size of each dot in the row plot of Figure 9 indicates the proportion of confidence regions of that volume among same-sized models. In Setting A and B, the confidence region volumes of same-sized models are the same. In Setting C, volumes of confidence regions of Berk and PoSI Box enlarge (hence smaller logpVolq{|M |q if the last covariate is included. Tables 4 and 5 show the numbers for the comparison of our method with selective inference when the selection procedure is forward stepwise and LARS, respectively. Sample splitting is a simple procedure that provides valid inference after selection as discussed in Section 1.3. We stress here that this is valid only for independent observations and that the model selected in the first split half could be different from the one selected in the full data. The comparison results with n 1000, p 500 and selection methods forward stepwise, LARS and BIC are summarized in Figure S.1. For sample splitting we have used the Bonferroni correction to obtain simultaneous inference for all coefficients in a model. Table 6 shows the comparison of our method with sample splitting.

Subfields: Statistics and Probability

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