Summary. Spatial linear models are popular for the analysis of data on a spatial lattice, but statistical techniques for selection of covariates and a neighbourhood structure are limited. Here we develop new methodology for simultaneous model selection and parameter estimation via penalized maximum likelihood under a spatial adaptive lasso. A computationally efficient algorithm is devised for obtaining approximate penalized maximum likelihood estimates. Asymptotic properties of penalized maximum likelihood estimates and their approximations are established. A simulation study shows that the method proposed has sound finite sample properties and, for illustration, we analyse an ecological data set in western Canada.
RE S E AR CHAs s e s s i n g t h e S p a t i a l Co n c e n t r a t i o n a n d T e mp o r a l P e r s i s t e n c e o f
P o v e r t y : I n d u s t r i a l S t r u c t u r e , Ra c i a l / E t h n i c Co mp o s i t i o n , a n d t h e Co mp l e x L i n k s t o P o v e r t y AB S TRACT
Previous studies have demonstrated that L-forms of bacteria may play a role in persistent, chronic, or recurrent urinary-tract infections. A 2-year program was initiated to determine the feasibility of culturing for L-forms on a routine basis, and to determine the effectiveness of such a program. In relation to the total number of specimens, few L-forms were actually isolated. In comparison with the amount of equipment and technician time required, the return was negligible; only 0.5% of all urine specimens were positive for L-forms. An increase to only 1.2% was noted when culturing for L-forms was limited to patients with a diagnosis of bacteriuria or pyelonephritis. It is recommended that this technique be reserved for those patients with a long history of recurrent urinary-tract infections, after other attempts to cure the patient have met with failure.
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