18 resultados para model order estimation


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This paper proposes a new estimator for the fixed effects ordered logit model. In contrast to existing methods, the new procedure allows estimating the thresholds. The empirical relevance and simplicity of implementation is illustrated in an application on the effect of unemployment on life satisfaction.

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Studies have shown that the discriminability of successive time intervals depends on the presentation order of the standard (St) and the comparison (Co) stimuli. Also, this order affects the point of subjective equality. The first effect is here called the standard-position effect (SPE); the latter is known as the time-order error. In the present study, we investigated how these two effects vary across interval types and standard durations, using Hellström’s sensation-weighting model to describe the results and relate them to stimulus comparison mechanisms. In Experiment 1, four modes of interval presentation were used, factorially combining interval type (filled, empty) and sensory modality (auditory, visual). For each mode, two presentation orders (St–Co, Co–St) and two standard durations (100 ms, 1,000 ms) were used; half of the participants received correctness feedback, and half of them did not. The interstimulus interval was 900 ms. The SPEs were negative (i.e., a smaller difference limen for St–Co than for Co–St), except for the filled-auditory and empty-visual 100-ms standards, for which a positive effect was obtained. In Experiment 2, duration discrimination was investigated for filled auditory intervals with four standards between 100 and 1,000 ms, an interstimulus interval of 900 ms, and no feedback. Standard duration interacted with presentation order, here yielding SPEs that were negative for standards of 100 and 1,000 ms, but positive for 215 and 464 ms. Our findings indicate that the SPE can be positive as well as negative, depending on the interval type and standard duration, reflecting the relative weighting of the stimulus information, as is described by the sensation-weighting model.

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nlcheck is a simple diagnostic tool that can be used after fitting a model to quickly check the linearity assumption for a given predictor. nlcheck categorizes the predictor into bins, refits the model including dummy variables for the bins, and then performs a joint Wald test for the added parameters. Alternative, nlcheck uses linear splines for the adaptive model. Support for discrete variables is also provided. Optionally, nlcheck also displays a graph of the adjusted linear predictions from the original model and the adaptive model