Explore the vast range of titles available.

We consider prediction of graft survival when a kidney from a deceased donor is transplanted into a recipient, with a focus on the variation of survival with degree of human leukocyte antigen (HLA) mismatch. Previous studies have used data from the Scientific Registry of Transplant Recipients (SRTR) to predict survival conditional on partial characterization of HLA mismatch. Whereas earlier studies assumed proportional hazards models, we used nonparametric regression methods. These do not make the unrealistic assumption that relative risks are invariant as a function of time since transplant, and hence should be more accurate. To refine the predictions possible with partial knowledge of HLA mismatch, it has been suggested that HaploStats statistics on the frequencies of haplotypes within specified ethnic/national populations be used to impute complete HLA types. We counsel against this, showing that it cannot improve predictions on average and sometimes yields suboptimal transplant decisions. We show that the HaploStats frequency statistics are nevertheless useful when combined appropriately with the SRTR data. Analysis of the ecological inference problem shows that informative bounds on graft survival probabilities conditional on refined HLA typing are achievable by combining SRTR and HaploStats data with immunological knowledge of the relative effects of mismatch at different HLA loci.

The political fragmentation following the 2008 Financial Crisis and its economic, social, political and institutional fall-out have led to a growing left-right polarisation of politics and a weakening of the middle ground. The effective number of parliamentary parties is at an all-time high both inthe Spanish Parliament (Congreso) and in the Valencian Autonomous Parliament (Corts). Voters are spoilt for choice and switch party more often. This paper uses transfer matrices to analyse the shifting voting patterns in the European, General, Regional, and Local elections held during 2019 in The Valencian Country. The most salient result is the ever-shifting pattern at each end of the political spectrum. On the right wing, there is the steady advance of Vox. On the left wing, UP and Compromís draw from virtually the same pool of fickle voters, with UP picking up most votes in national elections and Compromís winning hands-down in regional and local elections.

The paper provides a new and more explicit formulation of the assumptions needed by the ordinary ecological regression to provide unbiased estimates and clarifies why violations of these assumptions will affect any method of ecological inference. Empirical evidence is obtained by showing that estimates provided by three main ecological inference methods are heavily biased when compared with multilevel logistic regression applied to a unique set of individual data on voting behaviour. The main findings of our paper have two important implications that can be extended to all situations where the assumptions needed to apply ecological inference are violated in the data: (i) only ecological inference methods that allow one to model the effect of covariates have a chance to produce unbiased estimates, and (ii) there are certain data generating mechanisms producing a kind of bias in ecological estimates that cannot be corrected by modelling the effect of covariates.

Political scientists are increasingly interested in the geographic distribution of political and economic phenomena. Unlike distribution measures at the individual level, geographic distributions depend on the “unit question” in which researchers choose the appropriate political subdivision to analyze, such as nations, subnational regions, urban and rural areas, or electoral districts. We identify concerns with measuring geographic distribution and comparing distributions within and across political units. In particular, we highlight the potential for threats to inference based on the modifiable areal unit problem (MAUP), whereby measuring concepts at different unit aggregations alters the observed value. We offer tangible options for researchers to improve their research design and data analysis to limit the MAUP. To help manage the measurement error when the unit of observation is unclear or appropriate data are not available, we introduce a new measure of geographic distribution that accounts for fluctuations in the scale and number of political units considered. We demonstrate using Monte Carlo simulations that our measure is more reliable and stable across political units than commonly used indicators because it reduces measurement fluctuations associated with the MAUP.

The diversity of the Asian American population presents challenges for theories of bloc voting, partisan voting, and descriptive representation. What cues (if any) do Asian American voters rely on? How informative are racial and partisan cues to Asian American voters. This article looks at the candidate preferences of Asian American voters in the 2018 election. I look at elections where an Asian American candidate was on the ballot and compare outcomes within district to the gubernatorial race (a race with no minorities on the ballot). I use surname-coded voter registration records and precinct-level vote returns to estimate Asian American candidate preferences as a racial group and by national-origin. I find strong evidence of national-origin preferences (i.e. Vietnamese for a Vietnamese candidate) among Asian American voters. In instances where the national-origin of the candidate and the national-origin of the voter did not align, voters seem to rely on partisan cues. National-origin preferences are sufficient enough that in one instance voters switched parties within the same election to vote for a candidate of the same national-origin. These findings have implications for theories of minority vote choice and challenges the existing literature on the strength of partisan cues (replication data can be found at: https://sites.google.com/view/vivienleung/research).

The general purpose of this work consists in to relate the statistical methods for the estimation of voter transitions rates based on aggregate data, with the problem of inferring the composition of the electorate in a democratic system in seven categories of voters once the second of two consecutive voting processes has been carried out. To know the electorate composition between stable and unstable voters is a matter of relevance to sociology and political science regarding comparative research. Available options to infer these values—electoral polls and panel surveys—present reliability issues arising from lack of recall or concealing on the voting behavior. In view of this situation, we propose an original estimation strategy consisting in to locate the unknown quantities within of a matrix whose sums of entries by rows and columns are known; based on this, such magnitudes can be estimated resorting to Ecological inference methods. The proposal was applied to the case of competition between political conglomerates in Chile for the period 1993–2009, using two types of estimation methods with aggregate data available in the free software R. One of those methods rendered results consistent with previous evidence proceeding from polls. We conclude that the proposed strategy can be replicable on a larger-scale application, even though these methods must, in parallel, remain subject to evaluation and improvement.

Ecological inference refers to the study of individuals using aggregate data and it is used in an impressive number of studies; it is well known, however, that the study of individuals using group data suffers from an ecological fallacy problem (Robinson in Am Sociol Rev 15:351–357, 1950). This paper evaluates the accuracy of two recent methods, the Rosen et al. (Stat Neerl 55:134–156, 2001) and the Greiner and Quinn (J R Stat Soc Ser A (Statistics in Society) 172:67–81, 2009) and the long-standing Goodman’s (Am Sociol Rev 18:663–664, 1953; Am J Sociol 64:610–625, 1959) method designed to estimate all cells of R × C tables simultaneously by employing exclusively aggregate data. To conduct these tests we leverage on extensive electoral data for which the true quantities of interest are known. In particular, we focus on examining the extent to which the confidence intervals provided by the three methods contain the true values. The paper also provides important guidelines regarding the appropriate contexts for employing these models.

Mental health outcomes may show wide contrasts in incidence or prevalence between ethnic or socio-economic groups, often for relatively infrequent events. To gauge such relativities, one ideally seeks age standardised comparisons, given that ethnic groups may differ in age structure, and that the events themselves often show wide age disparities in risk. It is also advantageous to provide a geographically disaggregated (e.g. neighbourhood) perspective on relative risk differences, with sampling densities (e.g. Poisson) appropriate to possibly infrequent events. Often only total disease counts (with no socio-demographic disaggregation) are available for neighbourhoods, though data on ethnic or social mix (e.g. Census data) are available from other sources. We consider in this paper a novel ecological inference method which can use such information, and which furthermore takes account of the impacts of neighbourhood age structure on health outcomes. We consider a case study to estimate age standardised relative risks for psychosis by neighbourhood and ethnicity. The analysis is for 6856 English neighbourhoods.

The analysis of aggregate data has received increasing attention in the statistical discipline over the past 20 years, with the ongoing development of a suite of techniques that are classified as ecological inference. Much of its development has been focused solely on estimating the cell frequencies in a 2 × 2 contingency table where only the marginal totals are given; an approach that has been received with mixed reviews. More recently, the focus has shifted toward analyzing the overall association structure, rather than on the estimation of cell frequencies. This article provides some insight into how informative the aggregate data in a single 2 × 2 contingency table are for assessing the association between the variables. This is achieved through the development of a new index, the aggregate informative index. This new index quantifies how much information, on a [0, 100] scale, is needed in the marginal information in a 2 × 2 contingency table to conclude that a statistically significant association exists between the variables. It is established that, unlike Pearson’s (and other forms of the) chi-squared statistic, this new index is immune to changes in the sample size. It is also shown that the new index remains stable when the 2 × 2 contingency table consists of extreme marginal information.

The link between objective facts and politically relevant beliefs is an essential mechanism for democratic accountability. Yet the bulk of empirical work on this topic measures objective facts at whatever geographic units are readily available. We investigate the implications of these largely arbitrary choices for predicting individual-level opinions. We show that varying the geographic resolution—namely aggregating economic data to different geographic units—influences the strength of the relationship between economic evaluations and local economic conditions. Finding that unemployment claims are the best predictor of economic evaluations, especially when aggregated at the commuting zone or media market level, we underscore the importance of the modifiable areal unit problem. Our methods provide an example of how applied scholars might investigate the importance of geography in their own research going forward.