Consistent and Non-Degenerate Model Specification Tests against Smooth Transition and Neural Network Alternatives

2008 ◽  
pp. 145 ◽  
Author(s):  
Hill
Keyword(s):  
Neural Network ◽  
Model Specification ◽  
Smooth Transition ◽  
Specification Tests ◽  
Degenerate Model

Provocation and the Strategy of Terrorist and Guerrilla Attacks

2015 ◽  
Vol 70 (1) ◽  
pp. 133-173 ◽  
Author(s):  
David B. Carter
Keyword(s):  
Empirical Model ◽  
Western Europe ◽  
Model Specification ◽  
Terrorist Attacks ◽  
Specification Tests ◽  
State Response ◽  
Target Theory ◽  
Response Groups ◽  
Using Data ◽  
State Responses

AbstractViolent nonstate groups are usually weaker than the states they target. Theory suggests that groups carefully condition their choice of tactics on anticipated state response. Yet scholars know very little about whether and how groups strategically plan attacks in anticipation of state response. Scholars do not know if and under what conditions groups employ violent tactics to provoke or avoid a forceful state response, although extant theory is consistent with both possibilities. Relatedly, there is little systematic evidence about why groups choose terrorist or guerrilla tactics and how this choice relates to anticipated state response. I develop a theoretical and empirical model of the interaction between groups and states that generates unique evidence on all three fronts. Using data on attacks in Western Europe from 1950 to 2004, I show that guerrilla attacks are sometimes associated with provoking forceful state response, whereas terrorist attacks are generally associated with avoiding forceful response. Groups effectively choose their tactics to avoid forceful state responses that are too damaging for themselves but provoke forceful responses that disproportionately harm civilians. These findings survive several robustness and model specification tests.

A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity

2002 ◽  
Vol 34 (4) ◽  
pp. 733-754 ◽  
Author(s):  
Antonio Páez ◽  
Takashi Uchida ◽  
Kazuaki Miyamoto
Keyword(s):  
Geographically Weighted Regression ◽  
Degrees Of Freedom ◽  
Likelihood Function ◽  
Error Variance ◽  
Model Specification ◽  
Weighted Regression ◽  
Regularity Conditions ◽  
Local Regression ◽  
Specification Tests ◽  
Geographically Weighted Regression Model

Geographically weighted regression (GWR) has been proposed as a technique to explore spatial parametric nonstationarity. The method has been developed mainly along the lines of local regression and smoothing techniques, a strategy that has led to a number of difficult questions about the regularity conditions of the likelihood function, the effective number of degrees of freedom, and in general the relevance of extending the method to derive inference and model specification tests. In this paper we argue that placing GWR within a different statistical context, as a spatial model of error variance heterogeneity, or what might be termed locational heterogeneity, solves these difficulties. A maximum-likelihood-based framework for estimation and inference of a general geographically weighted regression model is presented that leads to a method to estimate location-specific kernel bandwidths. Moreover, a test for locational heterogeneity is derived and its use exemplified with a case study.

CONSISTENT MODEL SPECIFICATION TESTS

2000 ◽  
Vol 16 (6) ◽  
pp. 1016-1041 ◽  
Author(s):  
Yanqin Fan ◽  
Qi Li
Keyword(s):  
Nonparametric Tests ◽  
Model Specification ◽  
Local Alternatives ◽  
Exponential Weight ◽  
Conditional Moment ◽  
Specification Tests ◽  
Consistent Model ◽  
Close Relationship ◽  
Complex Valued ◽  
Theoretical Results

We point out the close relationship between the integrated conditional moment tests in Bierens (1982, Journal of Econometrics 20, 105–134) and Bierens and Ploberger (1997, Econometrica 65, 1129–1152) with the complex-valued exponential weight function and the kernel-based tests in Härdle and Mammen (1993, Annals of Statistics 21, 1926–1947), Li and Wang (1998, Journal of Econometrics 87, 145–165), and Zheng (1996, Journal of Econometrics 75, 263–289). It is well established that the integrated conditional moment tests of Bierens (1982) and Bierens and Ploberger (1997) are more powerful than kernel-based nonparametric tests against Pitman local alternatives. In this paper we analyze the power properties of the kernel-based tests and the integrated conditional moment tests for a sequence of “singular” local alternatives, and show that the kernel-based tests can be more powerful than the integrated conditional moment tests for these “singular” local alternatives. These results suggest that integrated conditional moment tests and kernel-based tests should be viewed as complements to each other. Results from a simulation study are in agreement with the theoretical results.

scholarly journals A GPU-Accelerated Machine Learning Framework for Molecular Simulation: HOOMD-blue with TensorFlow

2019 ◽  
Author(s):  
Rainier Barrett ◽  
Maghesree Chakraborty ◽  
Dilnoza Amirkulova ◽  
Heta Gandhi ◽  
Andrew White
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Model Building ◽  
Dynamics Simulation ◽  
Model Specification ◽  
Force Matching ◽  
Lennard Jones ◽  
Chemistry Research ◽  
The Rich

<div> <div> <div> <p>As interest grows in applying machine learning force-fields and methods to molecular simulation, there is a need for state-of-the-art inference methods to use trained models within efficient molecular simulation engines. We have designed and implemented software that enables integration of a scalable GPU-accelerated molecular mechanics engine, HOOMD-blue, with the machine learning (ML) TensorFlow package. TensorFlow is a GPU-accelerated, scalable, graph-based tensor computation model building package that has been the implementation of many recent innovations in deep learning and other ML tasks. TensorFlow models are constructed in Python and can be visualized or debugged using the rich set of tools implemented in the TensorFlow package. In this article, we present four major examples of tasks this software can accomplish which would normally require multiple different tools: (1) we train a neural network to reproduce a force field of a Lennard-Jones simulation; (2) we perform online force matching of methanol; (3) we compute the maximum entropy bias of a Lennard-Jones collective variable; (4) we calculate the scattering profile of an ongoing TIP4P water molecular dynamics simulation. This work should accelerate both the design of new neural network based models in computational chemistry research and reproducible model specification by leveraging a widely-used ML package.</p></div></div></div>

Research on Direct Torque Control Adaptive Fuzzy Neural Network Controller of Permanent Magnet Synchronous Motor

2014 ◽  
Vol 989-994 ◽  
pp. 2815-2819
Author(s):  
Chao Fan Lu ◽  
Hong Bin Yu
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Hybrid System ◽  
Fuzzy Neural Network ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Learning Ability ◽  
Smooth Transition ◽  
Neural Network Controller ◽  
Fuzzy Neural

Has the advantages of quick response of PMSM using the method of DTC, but will make the high torque and big magnetic flux linkage ripples. In order to solve this problem, using the fuzzy neural network hybrid system to replace the traditional hysteresis controller, Strong learning ability and fuzzy logic in handling uncertain information has the adaptive ability of neural network, the fuzzy neural network hybrid system to produce the expected voltage vector, the speed of a smooth transition of permanent magnet synchronous motor. The proposed method is validated by simulation under external disturbances in motor is very effective to reduce the ripple of torque and flux, the speed of the fast response and smooth transition.

scholarly journals Geography and Infrastructure Dimension on Poverty in Riau Province: Data Panel Approach at the Village Level

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Heffi Christya Rahayu ◽  
Julianus Johnny Sarungu ◽  
Lukman Hakim ◽  
Albertus Maqnus Soesilo ◽  
Bhimo Rizky Samudro ◽  
...  
Keyword(s):  
Cross Sections ◽  
Transportation Network ◽  
Least Square ◽  
Model Specification ◽  
Significant Positive Effect ◽  
Poverty Level ◽  
Specification Tests ◽  
Pooled Data ◽  
The Village ◽  
Positive Effect

This study aims to analyze the geographical and infrastructure aspects of poverty in districts and cities in Riau Province, Indonesia. This study used statistical data from 2003, 2006, 2009, 2011 and 2014 correspondingly published by the Central Bureau of Statistics (BPS), Indonesia. There are 1,687 villages collected into cross-sections and time series or pooled data. This study proposed a geographical perspective to identify the poverty level. Based on the model specification tests through the three analyses with the pooled least square (PLS), the study found that the regression of the determinant coefficient (R2) is 0.0492, indicating that the geographical and infrastructure variables can explain 49.2 % of the output percentage variation in poverty levels. The telephone network is a factor that has a significant positive effect on the poverty variable. One variable used, the river as a transportation network, has a significant positive effect, which indicates that the usage of rivers for the purpose of transportation can increase the poverty level in the Riau province of Indonesia.

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