5,877 research outputs found
Mean-parametrized Conway-Maxwell-Poisson regression models for dispersed counts
Conway-Maxwell-Poisson (CMP) distributions are flexible generalizations of
the Poisson distribution for modelling overdispersed or underdispersed counts.
The main hindrance to their wider use in practice seems to be the inability to
directly model the mean of counts, making them not compatible with nor
comparable to competing count regression models, such as the log-linear
Poisson, negative-binomial or generalized Poisson regression models. This note
illustrates how CMP distributions can be parametrized via the mean, so that
simpler and more easily-interpretable mean-models can be used, such as a
log-linear model. Other link functions are also available, of course. In
addition to establishing attractive theoretical and asymptotic properties of
the proposed model, its good finite-sample performance is exhibited through
various examples and a simulation study based on real datasets. Moreover, the
MATLAB routine to fit the model to data is demonstrated to be up to an order of
magnitude faster than the current software to fit standard CMP models, and over
two orders of magnitude faster than the recently proposed hyper-Poisson model.Comment: To appear in Statistical Modelling: An International Journa
SEQUENCING AND THE SUCCESS OF GRADUALISM: EMPIRICAL EVIDENCE FROM CHINA'S AGRICULTURAL REFORM
This paper provides evidence regarding gains to agricultural market liberalization in China. We empirically identify the different effects that incentive reforms and gradual market liberalization have on China's agricultural economy during its transition period. We find that average gains within the agricultural sector to incentive reform exceed gains to market liberalization by a factor of ten. Our method of analyzing the effects of transition policies on economic performance can be generalized to other reform paths in other transition economies.Agricultural and Food Policy,
Sequencing and the Success of Gradualism: Empirical Evidence from China's Agricultural Reform
This paper provides evidence regarding gains to agricultural market liberalization in China. We empirically identify the different effects that incentive reforms and gradual market liberalization have on China's agricultural economy during its transition period. We find that average gains within the agricultural sector to incentive reform exceed gains to market liberalization by a factor of ten. Our method of analyzing the effects of transition policies on economic performance can be generalized to other reform paths in other transition economies.China, agriculture; adjustment cost model; economic transition
Sequencing and the Success of Gradualism: Empirical Evidence from China's Agricultural Reform
This paper provides evidence regarding gains to agricultural market liberalization in China. We empirically identify the different effects that incentive reforms and gradual market liberalization have on China's agricultural economy during its transition period. We find that average gains within the agricultural sector to incentive reform exceed gains to market liberalization by a factor of ten. Our method of analyzing the effects of transition policies on economic performance can be generalized to other reform paths in other transition economies.China, agriculture, adjustment cost model, economic transition
Sequencing and the Success of Gradualism: Empirical Evidence from China's Agricultural Reform
This paper provides evidence regarding gains to agricultural market liberalization in China. We empirically identify the different effects that incentive reforms and gradual market liberalization have on China's agricultural economy during its transition period. We find that average gains within the agricultural sector to incentive reform exceed gains to market liberalization by a factor of ten. Our method of analyzing the effects of transition policies on economic performance can be generalized to other reform paths in other transition economies.China, agriculture, transition, profit function estimation Creation Date: 2002-06-10
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model
for generating novel image captions. It directly models the probability
distribution of generating a word given previous words and an image. Image
captions are generated by sampling from this distribution. The model consists
of two sub-networks: a deep recurrent neural network for sentences and a deep
convolutional network for images. These two sub-networks interact with each
other in a multimodal layer to form the whole m-RNN model. The effectiveness of
our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K,
Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In
addition, we apply the m-RNN model to retrieval tasks for retrieving images or
sentences, and achieves significant performance improvement over the
state-of-the-art methods which directly optimize the ranking objective function
for retrieval. The project page of this work is:
www.stat.ucla.edu/~junhua.mao/m-RNN.html .Comment: Add a simple strategy to boost the performance of image captioning
task significantly. More details are shown in Section 8 of the paper. The
code and related data are available at https://github.com/mjhucla/mRNN-CR ;.
arXiv admin note: substantial text overlap with arXiv:1410.109
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