We compare the individual-based "threshold model" of innovation diffusion in the version which has been studied by Young (1998), with an aggregate model we derived from it. This model allows us to formalise and test hypotheses on the influence of individual characteristics upon global evolution. The classical threshold model supposes that an individual adopts a behaviour according to a trade-of...

A new probability distribution is proposed in this paper. This has support on the interval and was obtained after transforming random variable with exponential distribution. The mode, quantile function, median, ordinary moments density function belongs to family of distributions are demonstrated. maximum likelihood method used obtain parameter estimate. regression model for median also proposed...

The paper studys the mathematical model between element marketization and technology progress though Chinese provinces data. Empirical test showed that capital and labor element marketization promote Chinese technology progress significantly. Finally, threshold model showed the existence of threshold range that capital and labor element marketization promoting TFP (total factor productivity) gr...

We compare the individual-based “threshold model” of innovation diffusion (Valente 95), in the version which has been studied by P. Young, and an aggregate deterministic model we constructed from it. The classical threshold model supposes that an individual adopts a behaviour according to a trade-off between a social pressure (the number of his neighbours adopting the behaviour) and a personal ...

We study an optimal nonparametric regression model for a threshold detector exposed to a noisy, subthreshold signal. The problem of recovering the signal is similar to that faced by neurons in nervous systems, although our model is intended to be normative rather than realistic. In our approach, the time-integrating activity of the neuron is modeled by kernel regression. Several aspects of the ...

Classical peaks over threshold analysis is widely used for statistical modeling of sample extremes, and can be supplemented by a model for the sizes of clusters of exceedances. Under mild conditions a compound Poisson process model allows the estimation of the marginal distribution of threshold exceedances and of the mean cluster size, but requires the choice of a threshold and of a run paramet...

We consider model selection in a hierarchical Bayes formulation of the sparse normal linear model in which individual variables have, independently, an unknown prior probability of being included in the model. The focus is on orthogonal designs, which are of particular importance in nonparametric regression via wavelet shrinkage. Empirical Bayes estimates of hyperparameters are easily obtained ...

We consider model selection in a hierarchical Bayes formulation of the sparse normal linear model in which individual variables have, independently, an unknown prior probability of being included in the model. The focus is on orthogonal designs, which are of particular importance in nonparametric regression via wavelet shrinkage. Empirical Bayes estimates of hyperparameters are easily obtained ...

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A good approximation to the integrate-and-fire model with diffusive noise can be obtained using a noisy threshold model. This allows the response of a population of noisy neurons to a current transient to be described using a linear filter. Here we apply these analytical results to the peristimulus time histogram (PSTH) of a single neuron. The effect of the noise on the PSTH in our model is sim...