Estimating Vote Transition Counts in Ecological Inference

<p><b>This thesis investigates responses in voting behaviour and media perceptions to the presence of media scandals about politicians and associated political parties during the 2017 New Zealand general election. A repeated measures design was used wherein 351 participants were recruited before the start of the election campaign, primarily from an Introductory Psychology course at Victoria University of Wellington. Follow-up surveys were conducted at three time points throughout the two month campaign. Participants were randomly allocated into one of two conditions for each follow-up survey. Half the participants were given a real news article to read about a media scandal, the other half read an article about a policy platform by the same political party. At the end of the election campaign, participants were asked about their voting behaviours. A second study was conducted after Labour Party leader, Jacinda Ardern, was announced Prime Minister with participants recruited via social media sites ‘Twitter’ and ‘Facebook’. In this study, 153 participants recalled information about scandals that were present in the media during the election campaign.</b></p> <p>Results showed that political scandals in news media do have an influence on voter perceptions, but not in an easily predictable way. Prior perceptions of political parties were the best predictors of who participants intended to vote for. Participants responded most strongly to public policy articles rather than scandal information, particularly those more knowledgeable of New Zealand’s political system, and therefore likely more engaged with politics in general. Further, there was evidence that information presented in the media influenced how participants viewed political parties that were not involved in the scandal, which is an important under a proportional voting system like MMP which requires understanding of the relationships between parties.</p> <p>Evidence was also found for a backlash effect towards the media wherein participants who were exposed to scandal information would displayed a decrease in trust towards the general media, consistent with the idea that one reason why voters may not respond negatively to scandal information reflects the decision that the source of the information is not credible. Future research should consider more targeted analysis on the different sources of news media, especially new media like blogs, social media, and entertainment news.</p>

A paper examining the factors contributing to the growing electoral support for the PDS (Party of Democratic Socialism) in post-unification elections in the former German Democratic Republic (East Germany) uses detailed data for Berlin's electoral districts to elucidate the micro-geography of the East-West divide in the PDS vote. Using two methods of ecological inference, King's and entropy maximization, the authors show that the explanation of the PDS support in Berlin elections in 1999 and 2001 can be attributed to the "Mauer in den Köpfen" (wall in people's heads) that is coincident with the division of the city along the former Berlin Wall. This geographic division far exceeds any socio-demographic explanation of the PDS vote. Journal of Economic Literature, Classification Numbers: H10, O10, R10. 9 figures, 4 tables, 98 references.

Global and localized neighborhood effects on public transit ridership in Baton Rouge, Louisiana

The voter participation rate in the 2024 Sukabumi City Regional Election has not reached the 80% target set by the General Election Commission (KPU), with an average achievement of only 69.78%. This low number shows that there are obstacles in people's political involvement, one of which is related to voter behavior. This study aims to analyze the influence of voter behavior on the level of participation in the Regional Elections. Voter behavior is studied through sociological, psychological, and rational approaches. In Sukabumi City, voter behavior tends to be rational-pragmatic, namely voting based on considerations of direct benefits and concrete personal interests. This research uses a mixed method, with data collection through surveys, interviews, and documentation. The results of the regression analysis showed that voter behavior had a significant effect on participation, both individually and collectively. The conclusion of this study states that voter behavior, especially those that are rational-pragmatic, contribute positively to increasing participation in the Sukabumi City Regional Elections

Count data are found in Ecological studies. The log-transformed strategy is commonly used in the count or rate data. The rate data are defined as the count data divided by a scale variable such as population at risk or an expected count. The log-transformed strategy is used to satisfy the parametric approach and simplify the model estimation. However, this strategy is not correct. The parameter estimation based on the log-transformed strategy could produce a biased estimate with a high standard error estimate. In this study, we are interested in evaluating the bias of parameter estimates based on the log-transformed strategy on the linear regression model. The generalized linear models have better performance in dealing with count data. However, some practitioners who are more familiar with the linear regression model prefer to use a log-transformed strategy and handle the zero cases by adding small values to zero observations. Simulation data from a Poisson distribution were used to compare the Poisson regression model and the linear regression model combined with the log-transformed strategy. The models were evaluated based on the bias and the root-mean-squared error statistics. We found that the linear regression with log-transformation strategy provided a high bias and a small value of root-mean-squared error, especially for small sample size and a small value of the count data. We also use real data set to explore more detail the uses of log-transformed strategy and compare it with Poisson regression.

In this article, we report the results of a study that tested a values-based method of predicting political election results. The study was carried out on the 2014 New Zealand General Election, randomly selecting a stratified sample from a consumer panel. The survey of 858 participants used open-ended questions to invoke and capture values relevant to the election. By using corpus linguistic analysis techniques, terms were ranked by weighting based on a log-frequency entropy method. Lexicons for Lasswell and Kaplan’s societal value framework reduced the corpus of term-weighted documents to a workable number of eight user-defined societal value-topics. The topics were regressed onto the individual voting decision using a multinomial logit (MNL) regression. The mean absolute deviation (MAD) from the actual vote was 1.8%, much less than the margin of error of 3.5% expected from sampling error alone. The methodology was successful in predicting the outcome for the minor parties with good accuracy, for example, the prediction for the then newly formed Internet-Mana was out by about 0.5%. The framing-balanced, value-based predictions exhibited reasonable stability, considering they were made six weeks before Election Day. Thus the values relevant to the voters and a good prediction of the voting behavior became evident ahead of the official campaign period, which started four weeks before Election Day in New Zealand. Our study concluded that the value-based prediction shows promise for improving the quality of political journalism and public engagement in the period of election campaigns, and will assist greatly in focusing public debate more on values that are influential on citizens’ voting decisions.

Molecular ecology regularly requires the analysis of count data that reflect the relative abundance of features of a composition (e.g., taxa in a community, gene transcripts in a tissue). The sampling process that generates these data can be modelled using the multinomial distribution. Replicate multinomial samples inform the relative abundances of features in an underlying Dirichlet distribution. These distributions together form a hierarchical model for relative abundances among replicates and sampling groups. This type of Dirichlet-multinomial modelling (DMM) has been described previously, but its benefits and limitations are largely untested. With simulated data, we quantified the ability of DMM to detect differences in proportions between treatment and control groups, and compared the efficacy of three computational methods to implement DMM-Hamiltonian Monte Carlo (HMC), variational inference (VI), and Gibbs Markov chain Monte Carlo. We report that DMM was better able to detect shifts in relative abundances than analogous analytical tools, while identifying an acceptably low number of false positives. Among methods for implementing DMM, HMC provided the most accurate estimates of relative abundances, and VI was the most computationally efficient. The sensitivity of DMM was exemplified through analysis of previously published data describing lung microbiomes. We report that DMM identified several potentially pathogenic, bacterial taxa as more abundant in the lungs of children who aspirated foreign material during swallowing; these differences went undetected with different statistical approaches. Our results suggest that DMM has strong potential as a statistical method to guide inference in molecular ecology.

The defeat of incumbents in Indonesia’s 2024 concurrent regional elections reflects deeper issues in local electoral dynamics. In Subang Regency, the incumbent pair—Ruhimat and Aceng Kudus—lost despite holding office for five years and benefiting from institutional and political capital. This study aims to examine the factors behind this electoral defeat, using a sequential explanatory mixed-methods approach. Quantitative data were collected through closed-ended questionnaires from 400 voters across Subang sub-districts, while qualitative insights were drawn from in-depth interviews with five key informants. The findings reveal that while several variables influenced the outcome—including public dissatisfaction, weak campaign messaging, and fragmented political alliances—the most significant factor was political money. A total of 69.75% of respondents admitted that financial incentives had a decisive influence on their voting decisions. These findings underscore how clientelism and vote-buying practices continue to undermine the performance-based evaluation of candidates. The study concludes that political money remains a powerful determinant in regional elections, with significant implications for democratic accountability and electoral reform in Indonesia.

The existence of local political parties in outline is a new alternative to the implementation of local democratic processes in Aceh are not specifically for Islamization issues. In the political record, the process of electing leaders in Aceh Province has been noted for three times (2006, 2012 and 2017). Aceh continues to move dynamically with political activities that are often rarely predictable or new conflicts that are sometimes expected to occur during democratic parties. This paper examines factors of voter's behavior and political identity in Aceh after the signing peace agreement in 2005 as well as analyzes the regional elections in 2017 and the Islamization issue within it. It will be explained that the behavior of voters in the Aceh regional election is very crucial. The election runs successfully by playing the identity of the political party in the Islamic shari'a area bringing issues in local implementation's policy. Finally, the crisis of trust that has been happening to national political parties has become one of the benchmarks for people to participate in the implementation of local democracy through the emergence of local political parties in Aceh.

Summary The ability to identify key ecological processes is important when solving applied problems. Increasingly, ecologists are adopting Akaike's information criterion (AIC) as a metric to help them assess and select among multiple process‐based ecological models. Surprisingly, however, it is still unclear how best to incorporate AIC into the selection process in order to address the trade‐off between maximizing the probability of retaining the most parsimonious model while minimizing the number of models retained. Ecological count data are often observed to be overdispersed with respect to best‐fitting models. Overdispersion is problematic when performing an AIC analysis, as it can result in selection of overly complex models which can lead to poor ecological inference. This paper describes and illustrates two approaches that deal effectively with overdispersion. The first approach involves modelling the causes of overdispersion implicitly using compound probability distributions. The second approach ignores the causes of overdispersion and uses quasi‐AIC (QAIC) as a metric for model parsimony. Simulations and a novel method that identifies the most parsimonious model are used to demonstrate the utility of the two overdispersion approaches within the context of two ecological examples. The first example addresses binomial data obtained from a study of fish survival (as related to habitat structure) and the second example addresses Poisson data obtained from a study of flower visitation by nectarivores. Applying either overdispersion approach reduces the chance of selecting overly complex models, and both approaches result in very similar ecological inference. In addition, inference can be made more reliable by incorporating model nesting into the selection process (i.e. identifying which models are special cases of others), as it reduces the number of models selected without significantly reducing the probability of retaining the most parsimonious models. Synthesis and applications. When data are overdispersed, inference can be improved by either modelling the causes of overdispersion or applying QAIC as a metric for model parsimony. Inference can also be improved by adopting a model filtering procedure based on how models are nested. The general simulation approach presented in this paper for identifying the most parsimonious model, as defined by information theory, should help to improve our understanding of the reliability of model selection when using AIC, and help the development of better selection rules.

Money politics, the practice of distributing material incentives to sway voter decisions, has long been a challenge in Indonesian elections. This study examines the influence of money politics on voter behavior during the regional head elections in South Sulawesi. Using a mixed-methods approach, the study combines quantitative surveys and qualitative interviews to assess the prevalence of money politics and its impact on voting choices. The findings reveal that 61.1% of respondents were exposed to financial inducements, and lower-income voters were particularly susceptible to material rewards. The study also highlights the socio-cultural reinforcement of money politics, with local leaders playing a key role in distributing incentives and social norms of reciprocity shaping voter behavior. Despite these challenges, some voters demonstrated ethical awareness, suggesting that voter education could play a role in mitigating the impact of money politics. The study contributes to the broader literature on clientelism, emphasizing the need for both legal reforms and socio-economic changes to address the root causes of money politics. Future research could investigate the effectiveness of voter education programs and explore broader reforms to enhance electoral integrity.

This research was conducted to analyze how voters behave in regional head elections in Indonesia, during the Covid-19 pandemic. The method used in this research is descriptive qualitative. Data collection was carried out by means of in-depth interviews with various informants who were directly involved in the election of regional heads, election committees, election supervisors and voters. In addition, data collection was also carried out by tracing documents relevant to the research topic, both documents originating from the election committee and supervisors. From the research that has been done, it has been revealed that the voters in the regional head election which took place in Sumenep Regency, Indonesia in 2020 behaved pragmatically and permissively towards money politics. This was caused by the poor economic conditions of voters, during the Covid-19 pandemic. This pragmatic behavior also encourages their presence to choose a pair of candidates who give money. Thus, the level of voter turnout and participation in regional head elections is higher than before the Covid-19 pandemic. This research has limitations in the scope of research, which is only carried out in Sumenep Regency, Indonesia in 2020, so it cannot represent Indonesian society in general.

When explaining electoral behaviour, cleavage theory with its practical consequences constitutes a traditional question in established Western democracies, but is just emerging in post-socialist countries of Central and Eastern Europe. The general objective of this study is to identify the spatial patterns of election results and inter-electoral shifts in support of politico-ideological blocs (measured by distribution of mandates, gained by parties at 2009 and 2013 regional elections) at the level of self-governing regions of Slovakia, and evaluate the impact of socio-political cleavages that these territorial discrepancies within the country are caused by. Elections for regional self-government held in Slovakia in 2013 are properly evaluated, when a total of 408 regional MPs were delegated to parliaments of eight NUTS3 level regions. Spatial patterns of numbers and inter-electoral changes of the mandates gained by individual political blocs representing the right wing, etatist stream, parties of ethnic orientation, so-called ‘Slovak coalition’ and independent candidates are displayed not only at the regional scale, but also at the hierarchically lower level of election districts (with 90 territorial units). For regional elections, socio-economic and ethnic cleavages are considered the most important determinants of spatial differentiation related to the voting behaviour of the electorate, and then distribution of power in regional politics, but the phenomenon of independent candidates has the potential to compete with their explanatory value increasingly.

MotivationThe negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modelling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics.ResultsThe GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing better performance to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to two published GP methods. We also provide the option of modelling additional dropout using a zero-inflated negative binomial. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic.Availability and implementationGPcounts is implemented using the GPflow library in Python and is available at https://github.com/ManchesterBioinference/GPcounts along with the data, code and notebooks required to reproduce the results presented here. The version used for this paper is archived at https://doi.org/10.5281/zenodo.5027066.Supplementary informationSupplementary data are available at Bioinformatics online.

This work presents an unsupervised learning algorithm, using the mesh method for computing the log-likelihood function. The multinomial Dirichlet distribution (MDD) is one of the widely used methods of modeling multicategorical count data with overdispersion. Recently, it has been shown that traditional numerical computation of the MDD log-likelihood function either results in instability or leads to long run times that make its use infeasible in case of large datasets. Thus, we propose to use the mesh algorithm that involves approximating the MDD log-likelihood function based on Bernoulli polynomials. Moreover, we extend the mesh algorithm approach for computing the log-likelihood function of a more flexible distribution, namely the multinomial generalized Dirichlet (MGD). We demonstrate the efficiency of this method in statistical inference, i.e., maximum likelihood estimation, for fitting finite mixture models based on MDD and MGD as efficient distributions for count data. Through a set of experiments, the proposed approach shows its merits in two real-world clustering problems, namely natural scenes categorization and facial expression recognition.