Giving Order to Districts: Estimating Voter Distributions with National Election Returns

2009 ◽  
Vol 17 (3) ◽  
pp. 215-235 ◽  
Author(s):  
Georgia Kernell
Keyword(s):  
Empirical Research ◽  
Bayesian Model ◽  
Voting Behavior ◽  
Strategic Behavior ◽  
Error Model ◽  
Congressional District ◽  
Squared Error ◽  
Out Of Sample ◽  
The Common ◽  
Direct Measures

Correctly measuring district preferences is crucial for empirical research on legislative responsiveness and voting behavior. This article argues that the common practice of using presidential vote shares to measure congressional district ideology systematically produces incorrect estimates. I propose an alternative method that employs multiple election returns to estimate voters' ideological distributions within districts. I develop two estimation procedures—a least squared error model and a Bayesian model—and test each with simulations and empirical applications. The models are shown to outperform vote shares, and they are validated with direct measures of voter ideology and out-of-sample election predictions. Beyond estimating district ideology, these models provide valuable information on constituency heterogeneity—an important, but often immeasurable, quantity for research on representatives— strategic behavior.

scholarly journals A Bayesian model integration for mutation calling through data partitioning

2019 ◽  
Vol 35 (21) ◽  
pp. 4247-4254 ◽  
Author(s):  
Takuya Moriyama ◽  
Seiya Imoto ◽  
Shuto Hayashi ◽  
Yuichi Shiraishi ◽  
Satoru Miyano ◽  
...  
Keyword(s):  
Hierarchical Model ◽  
Bayesian Model ◽  
Information Sources ◽  
Tumor Model ◽  
Bayesian Hierarchical Model ◽  
Generative Models ◽  
Error Model ◽  
Model Integration ◽  
Bayesian Hierarchical ◽  
Model Based

Abstract Motivation Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled. Results We proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets. Availability and implementation https://github.com/takumorizo/OHVarfinDer. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Laboratory Experiment for Analyzing Electors’ Strategic Behavior in a First-Past-the-Post System

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1081
Author(s):  
Andrei Chiriță ◽  
Camelia Delcea
Keyword(s):  
Laboratory Experiment ◽  
Voting Behavior ◽  
Party System ◽  
Strategic Behavior ◽  
Real Life ◽  
Modern Society ◽  
Electoral System ◽  
Payoff Function ◽  
Voting System ◽  
Duverger's Law

As it is well acknowledged that the electoral system is one of the fundamental rocks of our modern society, the behavior of electors engaged in a voting system is of the utmost importance. In this context, the goal of the study is to model the behavior of voters in a first-past-the-post system and to analyze its consequences on a party system. Among the assumptions of this study is Duverger’s law, which states that first-past-the-post systems favor a two-party system as the voters engage in tactical voting, choosing to vote in favor of a less preferred candidate who has better odds of winning. In order to test this assumption and to better analyze the occurrence of the strategic behavior, a laboratory experiment was created. A total of 120 persons participated in the study. An asymmetrical payoff function was created to value the voters’ preference intensity. As a result, it was observed that as voters got used to the voting system, they engaged in more tactical voting behavior in order to either maximize the gain or minimize the loss of their choice. Moreover, the iterations where voters started displaying tactical behavior featured a clustering around two main choices. The obtained results are consistent with both the empirical results of real-life elections and Duverger’s law. A further discussion regarding the change in voters’ choice completes the analysis on the strategic behavior.

scholarly journals Application of a Vine Copula for Multi-Line Insurance Reserving

Risks ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 111
Author(s):  
Himchan Jeong ◽  
Dipak Dey
Keyword(s):  
Bayesian Model ◽  
Marginal Distribution ◽  
Development Model ◽  
Model Diagnostics ◽  
Risk Capital ◽  
Dependence Modeling ◽  
Insurance Portfolio ◽  
Out Of Sample ◽  
Vine Copula ◽  
A Company

This article introduces a novel use of the vine copula which captures dependence among multi-line claim triangles, especially when an insurance portfolio consists of more than two lines of business. First, we suggest a way to choose an optimal joint loss development model for multiple lines of business that considers marginal distribution, vine copula structure, and choice of family for each pair of copulas. The performance of the model is also demonstrated with Bayesian model diagnostics and out-of-sample validation measures. Finally, we provide an implication of the dependence modeling, which allows a company to analyze and establish the risk capital for whole portfolio.

Using Bayesian model averaging to improve ground motion predictions

2019 ◽  
Vol 220 (2) ◽  
pp. 1368-1378
Author(s):  
M Bertin ◽  
S Marin ◽  
C Millet ◽  
C Berge-Thierry
Keyword(s):  
Ground Motion ◽  
Model Uncertainty ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Selection Process ◽  
Hazard Analysis ◽  
Model Averaging ◽  
Likelihood Estimation ◽  
Predictive Performance ◽  
Out Of Sample

SUMMARY In low-seismicity areas such as Europe, seismic records do not cover the whole range of variable configurations required for seismic hazard analysis. Usually, a set of empirical models established in such context (the Mediterranean Basin, northeast U.S.A., Japan, etc.) is considered through a logic-tree-based selection process. This approach is mainly based on the scientist’s expertise and ignores the uncertainty in model selection. One important and potential consequence of neglecting model uncertainty is that we assign more precision to our inference than what is warranted by the data, and this leads to overly confident decisions and precision. In this paper, we investigate the Bayesian model averaging (BMA) approach, using nine ground-motion prediction equations (GMPEs) issued from several databases. The BMA method has become an important tool to deal with model uncertainty, especially in empirical settings with large number of potential models and relatively limited number of observations. Two numerical techniques, based on the Markov chain Monte Carlo method and the maximum likelihood estimation approach, for implementing BMA are presented and applied together with around 1000 records issued from the RESORCE-2013 database. In the example considered, it is shown that BMA provides both a hierarchy of GMPEs and an improved out-of-sample predictive performance.

The Comparison of GARCH and ANN Model for Forecasting Volatility: Evidence based on Indian Stock Markets

2020 ◽  
Vol 14 (2) ◽  
pp. 103-121
Author(s):  
MUNEER SHAIK ◽  
Aditya Sejpal
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Absolute Error ◽  
Series Data ◽  
Ann Model ◽  
Squared Error ◽  
Out Of Sample ◽  
Indian Stock Market ◽  
Forecasting Volatility ◽  
Better Than

In this paper, we study the performance of the Artificial Neural Networks (ANNs) and GARCH modelsto predict the volatility of the Indian stock market indices namely, NIFTY 50, NIFTY Bank and NIFTYFMCG. We have used the GARCH (1,1) and Recurrent Neural Network, a type of neural network whichis widely used for predicting time series data. The purpose of the study is to investigate if the ArtificialNeural Networks perform better than the traditional GARCH (1,1) model. An out of sample testingmethodology is applied to the most recent 20 percent of the observations for all the three indices. Wehave used Root Means Squared Error (RMSE) and Mean Absolute Error (MAE) as metrics to evaluatethe volatility predicting performances of the models. The results show no clear evidence of ANN modelperforming better than GARCH model for any of the three indices. ANNs may prove to be betterindicators in periods with low volatility while its performance deteriorated in periods with highvolatility.

Magic of signs: a non-local interpretation of homeopathy

2000 ◽  
Vol 89 (03) ◽  
pp. 127-140 ◽  
Author(s):  
H Walach
Keyword(s):  
Clinical Research ◽  
Empirical Research ◽  
Informational Content ◽  
Self Healing ◽  
High Dilution ◽  
Working Hypothesis ◽  
Non Local ◽  
The Common ◽  
Homeopathic Remedies ◽  
Repetitive Pattern

AbstractAmong homeopaths the common idea about a working hypothesis for homeopathic effects seems to be that, during the potentization process, ‘information’ or ‘energy’ is being preserved or even enhanced in homeopathic remedies. The organism is said to be able to pick up this information, which in turn will stimulate the organism into a self-healing response. According to this view the decisive element of homeopathic therapy is the remedy which locally contains and conveys this information. I question this view for empirical and theoretical reasons. Empirical research has shown a repetitive pattern, in fundamental and clinical research alike: there are many anomalies in high-dilution research and clinical homeopathic trials which will set any observing researcher thinking. But no single paradigm has proved stable enough in order to produce repeatable results independent of the researcher. I conclude that the database is too weak and contradictory to substantiate a local interpretation of homeopathy, in which the remedy is endowed with causal-informational content irrespective of the circumstances. I propose a non-local interpretation to understand the anomalies along the lines of Jung's notion of synchronicity and make some predictions following this analysis.

scholarly journals Rationalization of Legally Determined Expenditure as a Condition for Strengthening Financial Accountability

2018 ◽  
Vol 3 (1) ◽  
pp. 82-93
Author(s):  
Eugeniusz Ruśkowski ◽  
Urszula Zawadzka-Pąk
Keyword(s):  
Empirical Research ◽  
Common Good ◽  
Methodological Issues ◽  
Polish Literature ◽  
Financial Accountability ◽  
The Common Good ◽  
Constitutional Principle ◽  
Constitutional Tribunal ◽  
The Common ◽  
The Relationship

The main purpose of this article is to analyse the relationship between financial accountability and legally determined expenditure. According to the adopted research hypothesis, increasing the financial accountability requires taking specific actions in the field of the legally determined expenditure. As the article is theoretical, it does not present the results of the empirical research; the formal-dogmatic method was used to interpret the content of legal acts and jurisprudence of the Constitutional Tribunal, as well as the non-obstructive method to analyse the foreign and Polish literature presenting the results of both theoretical and empirical research. In the article, having presented in the introduction the methodological issues, first, the principle of common good, the financial accountability, and the legally determined expenditure will be first explained. Next, the solutions for the rationalization of the legally determined expenditure will be proposed. We conclude that their implementation should increase the financial accountability to strengthen the constitutional principle of the common good.

scholarly journals The Power of Context in Networks: Ideal Point Models with Social Interactions

2019 ◽  
Author(s):  
Mohammad T. Irfan ◽  
Tucker Gordon
Keyword(s):  
Voting Behavior ◽  
Strategic Behavior ◽  
Ideal Point ◽  
Limited Attention ◽  
Point Model ◽  
Real World Data ◽  
Strategic Behaviors ◽  
Using Data ◽  
Political Spectrum ◽  
The Ideal

Game theory has been widely used for modeling strategic behaviors in networked multiagent systems. However, the context within which these strategic behaviors take place has received limited attention. We present a model of strategic behavior in networks that incorporates the behavioral context, focusing on the contextual aspects of congressional voting. One salient predictive model in political science is the ideal point model, which assigns each senator and each bill a number on the real line of political spectrum. We extend the classical ideal point model with network-structured interactions among senators. In contrast to the ideal point model's prediction of individual voting behavior, we predict joint voting behaviors in a game-theoretic fashion. The consideration of context allows our model to outperform previous models that solely focus on the networked interactions with no contextual parameters. We focus on two fundamental questions: learning the model using real-world data and computing stable outcomes of the model with a view to predicting joint voting behaviors and identifying most influential senators. We demonstrate the effectiveness of our model through experiments using data from the 114th U.S. Congress.

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