many empirical papers have applied a semi-parametric two-stage procedure named tobit model to investigate the sources of inefficiency in different industries over the last two decades. using this approach in small samples has recently been criticized for a possible bias in its results. in very recent papers simar and wilson (2007) have tackled this problem by suggesting an alternative bootstrap...

In this paper, the author designs an efficient method for imputing iteratively missing target values with semi-parametric kernel regression imputation, known as the semi-parametric iterative imputation algorithm (SIIA). While there is little prior knowledge on the datasets, the proposed iterative imputation method, which impute each missing value several times until the algorithms converges in ...

Mean-Shift (MS) is a powerful non-parametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets. In this paper, for the purpose of algorithm speedup, we develop an agglomerative MS clustering method called Agglo-MS, along with its mode-seeking ability and convergence property analysis. Our method is built upon a...

Nonparametric resampling methods such as Direct Sampling are powerful tools to simulate new datasets preserving important data features spatial patterns from observed while using only minimal assumptions. However, cannot generate extreme events beyond the range of values. We here propose value theory for stochastic processes extrapolate towards yet unobserved high quantiles. Original first enri...

We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references ...