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We develop a latent variable selection method for multidimensional item response theory models. The proposed method identifies latent traits probed by items of a multidimensional test. Its basic strategy is to impose an [Formula: see text] penalty term to the log-likelihood. The computation is carried out by the expectation-maximization algorithm combined with the coordinate descent algorithm. Simulation studies show that the resulting estimator provides an effective way in correctly identifying the latent structures. The method is applied to a real dataset involving the Eysenck Personality Questionnaire.

An investigation of the acetylation of rice straw with acetic anhydride at 100 and 120 degrees C for 1-4 h with four tertiary amine catalysts (pyridine, 4-dimethylaminopyridine, N-methylpyrrolidine, and N-methylpyrrolidinone) or without catalyst in a solvent-free system was undertaken, and the extent of acetylation was measured by weight percent gain, which increased with the extent of reaction time and temperature and the amounts of catalyst used. 4-Dimethylaminopyridine was found to be the most effective catalyst of those studied. At a concentration of 7% of the catalyst in acetic anhydride, a weight percent gain of 15.4% was realized, compared with 11.2% for the noncatalyst reaction, after 0.5 h of exposure to the system at 120 degrees C. Characterization of acetylated straw was performed by FT-IR, CP MAS (13)C NMR, and thermal studies. Interestingly, the acetylated straw is significantly hydrophobic and does not get wet with water, thereby offering potential for the better utilization of cheap waste materials as natural sorbents in oil cleanup.