Density estimation for spatial linear processes

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Local linear spatial quantile regression (2009, background): “Several recent papers (among which [8,22,23,48,49] deal with the related problem of estimating the density f of a random field of the form {Xi; i ∈ Z }, whereas [19,24,35,36] consider the estimation of spatial mean regression functions).”
Local linear spatial regression (2004, background): “Several recent papers [e.g., Tran (1990), Tran and Yakowitz (1993), Carbon, Hallin and Tran (1996), Hallin, Lu and Tran (2001, 2004), Biau (2003) and Biau and Cadre (2004)] deal with the related problem of estimating the density f of a random field of the form {Xi; i ∈ ZN}, or the prediction problem”
Asymptotic theory for nonparametric regression with spatial data (2011, background): “Notice that Xn i;j=1;i 6=j ij = Xni 1 ji=1 ni;i=1;2 Cov fv(0; 0); v(j1; j2)g : Tran (1990), Hallin, Lu, and Tran (2001, 2004b), for example, established consistency results for kernel density estimates with lattice spatial data, under di¤erent conditions from those in Theorem 3.”
Estimation in semiparametric spatial regression (2006, methodology): “ear processes, without imposing the less veriﬁable mixing conditions. The asymptotic normality of the kernel density estimator was also established for spatial linear processes by Hallin, Lu and Tran [14]. Finally, the NW kernel method and the local linear spatial conditional regressor were treated ”