We investigate some aspects of the problem of the estimation of birth distributions (BDs) in multi-type Galton–Watson trees (MGWs) with unobserved types. More precisely, we consider two-type MGWs called spinal-structured trees. This kind of tree is characterized by a spine of special individuals whose BD $\nu$ is different from the other individuals in the tree (called normal, and whose BD is denoted by $\mu$). In this work, we show that even in such a very structured two-type population, our ability to distinguish the two types and estimate $\mu$ and $\nu$ is constrained by a trade-off between the growth-rate of the population and the similarity of $\mu$ and $\nu$. Indeed, if the growth-rate is too large, large deviation events are likely to be observed in the sampling of the normal individuals, preventing us from distinguishing them from special ones. Roughly speaking, our approach succeeds if $r\lt \mathfrak{D}(\mu,\nu)$, where r is the exponential growth-rate of the population and $\mathfrak{D}$ is a divergence measuring the dissimilarity between $\mu$ and $\nu$.

We present a method for estimating the ideology of political YouTube videos. The subfield of estimating ideology as a latent variable has often focused on traditional actors such as legislators, while more recent work has used social media data to estimate the ideology of ordinary users, political elites, and media sources. We build on this work to estimate the ideology of a political YouTube video. First, we start with a matrix of political Reddit posts linking to YouTube videos and apply correspondence analysis to place those videos in an ideological space. Second, we train a language model with those estimated ideologies as training labels, enabling us to estimate the ideologies of videos not posted on Reddit. These predicted ideologies are then validated against human labels. We demonstrate the utility of this method by applying it to the watch histories of survey respondents to evaluate the prevalence of echo chambers on YouTube in addition to the association between video ideology and viewer engagement. Our approach gives video-level scores based only on supplied text metadata, is scalable, and can be easily adjusted to account for changes in the ideological landscape.

The study of coup-proofing holds significant importance in political science as it offers insights into critical topics such as military coups, authoritarian governance, and international conflicts. However, due to the multifaceted nature of coup-proofing and empirical inconsistencies with existing indicators, there is a need for a more profound understanding and a new measurement methodology. We propose a new measure of the extent of coup-proofing, utilizing a Bayesian item response theory. We estimate the extent of coup-proofing using a sample of 76 countries between 1965 and 2005 and theoretically relevant observed indicators. The findings from the estimation demonstrate that the extent of coup-proofing varies across regime type, country, and time. Furthermore, we verify the construct validity of our measurement.

In the present study, we evaluated the effect of process-induced common bean hardness on structural properties of in vivo generated boluses and the consequences for in vitro starch digestion. Initially, the impact of human mastication on the particle size distribution (PSD) of oral boluses from common beans with different process-induced hardness levels was investigated through a mastication study. Then the effect of structural properties of selected boluses on in vitro starch digestion kinetics was assessed. For a particular process-induced hardness level, oral boluses had similar PSD despite differences in masticatory parameters between participants of the mastication study. At different hardness levels, a clear effect of processing (P<0·0001) was observed. However, the effect of mastication behaviour (P=0·1141) was not significant. Two distinctive fractions were present in all boluses. The first one was a cotyledon-rich fraction consisting of majorly small particles (40–125 µm), which could be described as individual cells based on microscopic observations. This fraction increased with a decrease in process-induced hardness. The second fraction (>2000 µm) mostly contained seed coat material and did not change based on hardness levels. The in vitro starch digestion kinetics of common bean boluses was only affected by process-induced hardness. After kinetic modelling, significant differences were observed between the reaction rate constant of boluses generated from the hardest beans and those obtained from softer ones. Overall this work demonstrated that the in vitro nutritional functionality of common beans is affected to a greater extent by structural properties induced by processing than by mechanical degradation in the mouth.

Optimization of production factors plays a central role in efficient milk production operations. Causal relationships between production parameters (health, fertility, feeding, performance and farm size) on the one hand and efficiency parameters on the other have been identified in several studies. In recent years, structural equation modelling (SEM) has not only gained importance in agriculture but also in milk production, providing the opportunity to investigate multilateral relationships. Additionally, SEM enables an estimation of parameters which are not themselves measurable, the so-called latent variables. The current study was based on the data of 943 branch settlements (including the years 2012 and 2013) of dairy farms keeping German Holstein cows in Schleswig-Holstein (Northern Germany) which provided a combination of the structural parameters, economic parameters and biological performance of the farms. An SEM using this combined data was applied to investigate the complexity of influences on efficiency parameters in milk production. Efficiency was sub-divided into and evaluated by two effect variables (economic efficiency and biological efficiency). Economic efficiency was defined as a conventional efficiency assessment criterion from full-cost accounting, whereas biological efficiency was used to evaluate the quality of herd management. Performance was identified as the key parameter for independent evaluation of efficiency by assessing biological (γ41 = 0.644) or economic efficiency (γ42 = 0.266). The SEM explained more than three times higher proportion of the variance in biological efficiency than in economic efficiency. The investigation proved the eligibility of partial least squares SEM for the evaluation of efficiency in milk production.

Recreation trends indicating an increasing demand for quality recreation experiences suggest the need for special consideration of quality in analysis of fee access recreation. By viewing quality as a subjective latent variable, this paper uses a simultaneous equation framework to consider the use of subjective versus objective appraisals of quality in fee-based recreation access analysis.

Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, most statistical models are formulated in terms of completely observed binary networks. Statistical analyses of FRN data with such models ignore the censored and ranked nature of the data and could potentially result in misleading statistical inference. To investigate this possibility, we compare Bayesian parameter estimates obtained from a likelihood for complete binary networks with those obtained from likelihoods that are derived from the FRN scheme, and therefore accommodate the ranked and censored nature of the data. We show analytically and via simulation that the binary likelihood can provide misleading inference, particularly for certain model parameters that relate network ties to characteristics of individuals and pairs of individuals. We also compare these different likelihoods in a data analysis of several adolescent social networks. For some of these networks, the parameter estimates from the binary and FRN likelihoods lead to different conclusions, indicating the importance of analyzing FRN data with a method that accounts for the FRN survey design.

Cognitive reserve, broadly conceived, encompasses aspects of brain structure and function that optimize individual performance in the presence of injury or pathology. Reserve is defined as a feature of brain structure and/or function that modifies the relationship between injury or pathology and performance on neuropsychological tasks or clinical outcomes. Reserve is challenging to study for two reasons. The first is: reserve is a hypothetical construct, and direct measures of reserve are not available. Proxy variables and latent variable models are used to attempt to operationalize reserve. The second is: in vivo measures of neuronal pathology are not widely available. It is challenging to develop and test models involving a risk factor (injury or pathology), a moderator (reserve) and an outcome (performance or clinical status) when neither the risk factor nor the moderator are measured directly. We discuss approaches for quantifying reserve with latent variable models, with emphasis on their application in the analysis of data from observational studies. Increasingly latent variable models are used to generate composites of cognitive reserve based on multiple proxies. We review the theoretical and ontological status of latent variable modeling approaches to cognitive reserve, and suggest research strategies for advancing the field. (JINS, 2011, 17, 593–601)