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Several previous studies of marine fish stocks have demonstrated time‐varying recruitment productivity and indicated that including time‐varying parameters can track process variation in recruitment. Few studies have synthesized signal‐to‐noise ratios and underlying reasons for time‐variation across stocks and regions. Using Peterman's productivity method (PPM), we provide a broad synthesis of time‐varying density‐independent productivity in 84 stocks across five regions of the United States. Of all stocks investigated, 50 were found to have time‐varying productivity, challenging assumptions on the stationarity of recruitment parameters and dependent reference points. Our results demonstrate the power of PPM for synthesizing the form and pattern of recruitment time‐variation among regions, including general summaries of directional change over time. Furthermore, our results show regional differences in time‐varying patterns, particularly the signal‐to‐noise ratio (SNR) of low‐ to high‐frequency variation. The SNR was lower in the California Current region than in two Atlantic regions and two Alaska regions. Generalized linear modelling used to synthesize results suggests that stocks with higher contrast in spawning stock biomass over time, standardized regardless of actual spawning stock size, were more likely to have time‐varying productivity than stocks with low contrast. The likelihood of time‐variation in productivity of a given stock was also found to be closely related to the autocorrelation of the recruitment time series. Such inter‐regional and inter‐stock comparisons of variation are vital in understanding the roles of local and global environmental change on fish productivity. [ABSTRACT FROM AUTHOR]

Relational event models (REMs) are the primary choice for the analysis of relational-event network data. However, the standard REM assumes static parameters, which hinders the modeling of time-varying dynamics. This assumption might be too restrictive in real-life scenarios, making a model that allows for time-varying parameters more valuable. We introduce a state-space extension of the relational event model as a way to tackle this problem. The model has three main attributes. First, it provides a statistical framework of the temporal change of the parameters. Second, it enables the forecasting of future parameter values (which can be utilized to simulate new networks that can account for temporal dynamics in out-of-sample predictions). Third, it requires smaller data structures to be loaded into computer memory compared to the standard REM; this makes the model easily scalable to large networks. We conduct empirical analyses on bike-sharing data, corporate communications, and interactions among socio-political actors to illustrate model usage and applicability. [ABSTRACT FROM AUTHOR]

Infectious respiratory diseases have been of interest in recent years for the great burden they place on health systems, for instance, the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) that caused the global COVID‐19 pandemic. As many of these diseases might require hospitalization and even intensive care unit (ICU) admission, understanding the joint dynamics of hospitalizations and ICU admissions across time and different groups of the population remains of great importance. We aim to understand the joint evolution of hospital and ICU admissions given COVID‐19 test‐positive cases in the province of Quebec, Canada. We obtain the daily counts, by age group, on the number of confirmed COVID‐19 cases, the number of hospitalizations and the number of ICU admissions due to COVID‐19, from March 2020 through October 2021 in Quebec. We propose a joint Bayesian generalized dynamic linear model for the number of hospitalizations and ICU admissions to study their temporal trends and possible associations with sex and age group. Additionally, we use transfer functions to investigate if there is a memory effect of the number of cases on hospitalizations across the different age groups. The results suggest that there is a clear distinction in the patterns of hospitalizations and ICU admissions across age groups and that the number of cases has a persistent effect on the rate of hospitalization. [ABSTRACT FROM AUTHOR]

Copyright of Society: Chinese Journal of Sociology / Shehui is the property of Society: Chinese Journal of Sociology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Quantifying sector‐resolved methane fluxes in complex emissions environments is challenging yet necessary to improve emissions inventories and guide policy. Here, we separate energy and agriculture sector emissions using a dynamic linear model analysis of methane, ethane, and ammonia data measured at a Northern Colorado site from November 2021 to January 2022. By combining these sector‐apportioned observations with spatially resolved inventories and Bayesian inverse methods, energy and agriculture methane fluxes are optimized across the study's ∼850 km2 sensitivity area. Energy sector fluxes are synthesized with previous literature to evaluate trends in energy sector methane emissions. Optimized agriculture fluxes in the study area were 3.5× larger than inventory estimates; we demonstrate this discrepancy is consistent with differences in the modeled versus real‐world spatial distribution of agricultural sources. These results highlight how sector‐apportioned methane observations can yield multi‐sector inventory optimizations in complex environments. Plain Language Summary: Improving our knowledge of the locations, magnitudes, and types of methane sources is important for implementing effective emissions mitigation technologies and regulations. Methane emissions are often challenging to quantify because a wide variety of sources can emit methane, and these disparate sources are often intermingled. We demonstrate how a dynamic linear model can use multi‐month time series of two tracer gases, ethane and ammonia, to effectively separate methane emissions from the energy and agriculture sectors. Incorporating these data into a Bayesian inverse analysis refines the magnitude and distribution of methane fluxes from each sector. Our analysis reveals that methane from agriculture is several times higher than inventory estimates. While this is in part due to the spatial distribution of sources, more monitoring is needed to improve agriculture emissions factors. Energy sector emissions factors optimized in this work are consistent with other regional studies of energy sector methane emissions. A synthesis of these works demonstrates a regional decline in energy sector emissions despite a concomitant increase in oil and gas extraction; however, current emissions are similar to 2008 estimates. Key Points: A dynamic linear model apportions energy and agriculture methane emissions from multi‐month trace gas measurements in Northern ColoradoAn estimated 0.4 ± 0.2 kg CH4 are emitted per barrel of oil equivalent produced, yielding a Wattenberg Field emission rate of 15 Mg CH4/hrOptimized agriculture methane emissions are higher than inventory predictions, in part due to mislocated fluxes in the inventory [ABSTRACT FROM AUTHOR]

Abstract Quantifying sector‐resolved methane fluxes in complex emissions environments is challenging yet necessary to improve emissions inventories and guide policy. Here, we separate energy and agriculture sector emissions using a dynamic linear model analysis of methane, ethane, and ammonia data measured at a Northern Colorado site from November 2021 to January 2022. By combining these sector‐apportioned observations with spatially resolved inventories and Bayesian inverse methods, energy and agriculture methane fluxes are optimized across the study's ∼850 km2 sensitivity area. Energy sector fluxes are synthesized with previous literature to evaluate trends in energy sector methane emissions. Optimized agriculture fluxes in the study area were 3.5× larger than inventory estimates; we demonstrate this discrepancy is consistent with differences in the modeled versus real‐world spatial distribution of agricultural sources. These results highlight how sector‐apportioned methane observations can yield multi‐sector inventory optimizations in complex environments.

Due to the high costs of monitoring environmental processes, measurements are commonly taken at different temporal scales. When observations are available at different temporal scales across different spatial locations, we name it temporal misalignment. Rather than aggregating the data and modeling it at the coarser scale, we propose a model that accounts simultaneously for the fine and coarser temporal scales. More specifically, we propose a spatiotemporal model that accounts for the temporal misalignment when one of the scales is the sum or average of the other by using the properties of the multivariate normal distribution. Inference is performed under the Bayesian framework, and uncertainty about unknown quantities is naturally accounted for. The proposed model is fitted to data simulated from different spatio‐temporal structures to check if the proposed inference procedure recovers the true values of the parameters used to generate the data. The motivating example consists of measurements of total pollen concentration across Toronto, Canada. The data were recorded daily for some sites and weekly for others. The proposed model estimates the daily measurements at sites where only weekly data was recorded and shows how the temporal aggregation of the measurements affects the associations with different covariates. [ABSTRACT FROM AUTHOR]

Wastewater sampling for the detection and monitoring of SARS-CoV-2 has been developed and applied at an unprecedented pace, however uncertainty remains when interpreting the measured viral RNA signals and their spatiotemporal variation. The proliferation of measurements that are below a quantifiable threshold, usually during non-endemic periods, poses a further challenge to interpretation and time-series analysis of the data. Inspired by research in the use of a custom Kalman smoother model to estimate the true level of SARS-CoV-2 RNA concentrations in wastewater, we propose an alternative left-censored dynamic linear model. Cross-validation of both models alongside a simple moving average, using data from 286 sewage treatment works across England, allows for a comprehensive validation of the proposed approach. The presented dynamic linear model is more parsimonious, has a faster computational time and is represented by a more flexible modelling framework than the equivalent Kalman smoother. Furthermore we show how the use of wastewater data, transformed by such models, correlates more closely with regional case rate positivity as published by the Office for National Statistics (ONS) Coronavirus (COVID-19) Infection Survey. The modelled output is more robust and is therefore capable of better complementing traditional surveillance than untransformed data or a simple moving average, providing additional confidence and utility for public health decision making. La détection et la surveillance du SARS-CoV-2 dans les eaux usées ont été développées et réalisées à un rythme sans précédent, mais l'interprétation des mesures de concentrations en ARN viral, et de leurs variations spatio-temporelles, pose question. En particulier, l'importante proportion de mesures en deçà du seuil de quantification, généralement pendant les périodes non endémiques, constitue un défi pour l'analyse de ces séries temporelles. Inspirés par un travail de recherche ayant produit un lisseur de Kalman adapté pour estimer les concentrations réelles en ARN de SARS-CoV-2 dans les eaux usées à partir de ce type de données, nous proposons un nouveau modèle linéaire dynamique avec censure à gauche. Une validation croisée de ces lisseurs, ainsi que d'un simple lissage par moyenne glissante, sur des données provenant de 286 stations d'épuration couvrant l'Angleterre, valide de façon complète l'approche proposée. Le modèle présenté est plus parcimonieux, offre un cadre de modélisation plus flexible et nécessite un temps de calcul réduit par rapport au Lisseur de Kalman équivalent. Les données issues des eaux usées ainsi lissées sont en outre plus fortement corrélées avec le taux d'incidence régional produit par le bureau des statistiques nationales (ONS) Coronavirus Infection Survey. Elles se montrent plus robustes que les données brutes, ou lissées par simple moyenne glissante, et donc plus à même de compléter la surveillance traditionnelle, renforçant ainsi la confiance en l'épidémiologie fondée sur les eaux usées et son utilité pour la prise de décisions de santé publique.

Wastewater sampling for the detection and monitoring of SARS-CoV-2 has been developed and applied at an unprecedented pace, however uncertainty remains when interpreting the measured viral RNA signals and their spatiotemporal variation. The proliferation of measurements that are below a quantifiable threshold, usually during non-endemic periods, poses a further challenge to interpretation and time-series analysis of the data. Inspired by research in the use of a custom Kalman smoother model to estimate the true level of SARS-CoV-2 RNA concentrations in wastewater, we propose an alternative left-censored dynamic linear model. Cross-validation of both models alongside a simple moving average, using data from 286 sewage treatment works across England, allows for a comprehensive validation of the proposed approach. The presented dynamic linear model is more parsimonious, has a faster computational time and is represented by a more flexible modelling framework than the equivalent Kalman smoother. Furthermore we show how the use of wastewater data, transformed by such models, correlates more closely with regional case rate positivity as published by the Office for National Statistics (ONS) Coronavirus (COVID-19) Infection Survey. The modelled output is more robust and is therefore capable of better complementing traditional surveillance than untransformed data or a simple moving average, providing additional confidence and utility for public health decision making. La détection et la surveillance du SARS-CoV-2 dans les eaux usées ont été développées et réalisées à un rhythme sans précédent, mais l'interprétation des mesures de concentrations en ARN viral, et de leurs variations spatio-temporelles, pose question. En particulier, l'importante proportion de mesures en deçà du seuil de quantification, généralement pendant les périodes non endémiques, constitue un défi pour l'analyse de ces séries temporelles. Inspirés par un travail de recherche ayant produit un lisseur de Kalman adapté pour estimer les concentrations réelles en ARN de SARS-CoV-2 dans les eaux usées à partir de ce type de données, nous proposons un nouveau modèle linéaire dynamique avec censure à gauche. Une validation croisée de ces lisseurs, ainsi que d'un simple lissage par moyenne glissante, sur des données provenant de 286 stations d'épuration couvrant l'Angleterre, valide de façon complète l'approche proposée. Le modèle présenté est plus parcimonieux, offre un cadre de modélisation plus flexible et nécessite un temps de calcul réduit par rapport au Lisseur de Kalman équivalent. Les données issues des eaux usées ainsi lissées sont en outre plus fortement corrélées avec le taux d'incidence régional produit par le bureau des statistiques nationales (ONS) Coronavirus Infection Survey. Elles se montrent plus robustes que les données brutes, ou lissées par simple moyenne glissante, et donc plus à même de compléter la surveillance traditionnelle, renforçant ainsi la confiance en l'épidémiologie fondée sur les eaux usées et son utilité pour la prise de décisions de santé publique. [ABSTRACT FROM AUTHOR]

Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying β-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated β time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately. [ABSTRACT FROM AUTHOR]

This study explores multi‐scale variability in the relationship between turbidity (Tn) and flow (Q) in 162 watersheds across the contiguous United States. Sites are selected where Tn acts as a good surrogate for suspended sediment concentration. We use dynamic linear models (DLMs) to infer time‐varying parameters of Tn‐Q rating curves at each site, and calibrate a hyper‐parameter of the DLM model (δ $\delta $) to quantify the degree of dynamicity in the rating curve relationship. The DLM can capture dynamics in the Tn‐Q relationship at the resolution of the data (daily in this study), enabling an analysis of the dynamics across time scales. Regional multivariate regressions are used to identify physiographic features that relate to the magnitude of δ $\delta $ and spectral signatures in the DLM parameters across sites. Results show that watersheds in the Midwest and Pacific Northwest tend to exhibit more variable Tn‐Q relationships, while these relationships are more stable in watersheds in the humid east and Lower Mississippi River basin. Stream network complexity, soil composition, perennial snow coverage, saturation‐excess overland flow, and modifications to the stream network are associated with the dynamicity in the Tn‐Q relationship. DLM parameters exhibit cyclic behavior at sub‐monthly, sub‐annual, and annual time scales at sites across the country, with annual cycling associated with basin features that reflect watershed sediment availability and the erosive power of rivers. Overall, our analysis highlights significant multi‐scale variability in Tn‐Q relationships across the nation, with important implications for how sediment dynamics should be measured and managed at the watershed‐scale. Plain Language Summary: Effective sediment management often requires accurate sediment yield predictions and forecasts. However, the predictive skill of various models often declines in regions where the sediment transport processes are variable across timescales. This study aims to improve our understanding of variability in the relationship between turbidity (Tn) and flow (Q) through time, focusing on sites where turbidity is a good proxy for suspended sediment concentration. We use regression models with parameters that can vary over time to quantify the variability in the Tn‐Q relationship and to highlight the presence of seasonal patterns in transport processes. We then explore how watershed characteristics influence the variability and seasonality in the Tn‐Q relationship. Our results suggest that stream network complexity, soil composition, perennial snow coverage, saturation‐excess overland flow, and modifications to the stream network influence the overall variability in the Tn‐Q relationship, while the seasonality is mainly associated with basin features that reflect watershed sediment availability and the erosive power of rivers. The results of the study have important implications for how sediment dynamics should be addressed in watershed management studies. Key Points: The dynamicity of sediment transport processes is quantified using dynamic linear rating curve models of turbidity and flowThe degree of rating curve dynamicity varies spatially and is partially influenced by natural and anthropogenic physiographic featuresDynamic rating curves exhibit cyclic behavior at various timescales across the US, which is also partially explained by basin features [ABSTRACT FROM AUTHOR]

In economic theory the Phillips curve presents the relationship between the unemployment rate and inflation rate. The inflation and unemployment rate bring important information about the stages of the economic cycle. This article attempts to find an answer to the question of whether the development of the difference between the unemployment and inflation rate, the so-called signal gap, may be an indicator of changes in the economic cycle. Quarterly data on the Czech Republic, France, Great Britain and the Republic of Korea were used to verify this hypothesis.

In this paper, COVID-19 cumulative cases are estimated with AKF based on total COVID-19 cases between January-September 9, 2020 in USA, Germany, United Kingdom, Italy, France, Russia, Brazil, India, Turkey, Spain, Peru, Colombia, South Africa, Argentina, Iran, Pakistan. The cumulative covid-19 cases time-series was modeled with a stochastic dynamic linear model (DLM). The estimation performance of the models is measured by the calculation of mean square error (MSE) and coefficient of determination (R2). Ca lculated MSE an d R² values showed that the model and AKF could be used to estimate the number of cases in these countries. In this study, firstly, the cumulative number of cases was estimated. Secondly, using these estimates number of daily cases was calculated. Thirdly, the reproduction number was obtained by using these number of daily cases. The model and estimation method used is suitable. The AKF algorithm uses only the number of cases in the last day. We propose that the model and estimation method under consideration is a convenient tool for calculating the reproduction number depending on time. [ABSTRACT FROM AUTHOR]

Predicting effects of climate on fitness‐linked phenotypic traits, such as body size, is important for the management and conservation of species in the face of global change. During sensitive life stages, small changes in mean trait values can have large effects on survival and population productivity.The transition from freshwater to saltwater by migrating anadromous fishes such as Pacific salmon (genus Oncorhynchus) is a critical life history transition, where survival is mediated by the size of migrating individuals. For salmon that spend extended periods rearing in freshwater, the size at ocean entry (i.e., smolt length) may be sensitive to changes in freshwater conditions shaped by biotic and abiotic factors, yet long‐term phenotypic time series for exploring these responses are rare.We reconstructed a four‐decade time series of smolt length from archives of returning adult scales to quantify population‐specific responses to climate and conspecific density in a small watershed.Dynamic linear modeling found that the relationship between a proxy for cohort density and smolt length at ocean entry was consistently negative, suggestive of competition. In contrast, there was a positive, yet dynamic, relationship between a proxy for density of emerging fry during the second summer of growth and smolt length.The effect of temperature on smolt length was not consistent between two populations of sockeye salmon O. nerka that likely use distinct habitats within the watershed. A positive relationship between smolt length and temperature was only detected for the less abundant, early returning population.Predictions of smolt length showed variable responses under scenarios of increasing temperature and high and low densities of conspecifics. Collectively, these results reveal population‐specific responses to temperature and density, suggesting that local habitat conditions may filter larger‐scale climate drivers. [ABSTRACT FROM AUTHOR]

House price indices (HPIs) are statistical measures of real estate price dynamics in defined geographic regions over defined periods of time. HPIs are important metrics that help policymakers, mortgage lenders, real estate investors, and bank regulators monitor market conditions and manage risk. HPIs that are local, reliable, and timely are essential in understanding connections between housing markets and the broader economy. In this article, we examine the algorithmic construction of Zillow's Home Value Index (ZHVI), an HPI built on black box machine learning algorithms. To provide deeper statistical insight into ZHVI than afforded by its black box construction, we develop a Bayesian generative meta‐model that approximates the black box construction of ZHVI series in 100 metropolitan areas (metros). Each ZHVI series is modeled with a global trend, a finite mixture of Gaussian processes, and a local component. We find that there are three shared dynamic patterns across the 100 markets in our analysis, and we utilize this shared latent structure to forecast ZHVI in each metro 12 months ahead. Our clustering strategy has two advantages: (i) it allows us to construct composite HPIs where member metros are learned from the data rather than predetermined; and (ii) it allows us to estimate the relative contributions of cluster‐level and metro‐specific components to a metro's ZHVI, providing a novel statistical attribution of real estate market dynamics. [ABSTRACT FROM AUTHOR]

In this paper we intend to examine the application of Kullback-Leibler, Hellinger and LINEX loss function in Dynamic Linear Model using the real price of oil for 106 years of data from 1913 to 2018 concerning the asymmetric problem in filtering and forecasting. We use DLM form of the basic Hoteling Model under Quadratic loss function, Kullback-Leibler, Hellinger and LINEX trying to address the results if we treat the ‘over-estimation’ and ‘under-estimation’ differently. So, we drive one-step-ahead forecast for Dynamic Linear Model under quadratic, LINEX and Kullback-Leibler losses in Bayesian context. With Normal posterior distribution, our results suggest that, the LINEX loss function may provide better forecasts than conventional Quadratic loss function, Hellinger and Kullback-Leibler loss function, especially in case of having volatility and time-varying parameters.JEL classification: C11, C22, C53, C61, Q47

Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to approximate the non-linear traffic states. Unlike a static linear regression model, the DLMs assume that their parameters are changing across time. We design a DLM with model parameters defined at each time unit to describe the spatio-temporal characteristics of time-series traffic data. Based on our DLM and its model parameters analytically trained using historical data, we suggest an optimal linear predictor in the minimum mean square error (MMSE) sense. We compare our prediction accuracy of travel time for freeways in California (I210-E and I5-S) under highly congested traffic conditions with those of other methods: the instantaneous travel time, k-nearest neighbor, support vector regression, and artificial neural network. We show significant improvements in the accuracy, especially for short-term prediction. [ABSTRACT FROM AUTHOR]

In economic theory the Phillips curve presents the relationship between the unemployment rate and inflation rate. The inflation and unemployment rate bring important information about the stages of the economic cycle. This article attempts to find an answer to the question of whether the development of the difference between the unemployment and inflation rate, the so-called signal gap, may be an indicator of changes in the economic cycle. Quarterly data on the Czech Republic, France, Great Britain and the Republic of Korea were used to verify this hypothesis. [ABSTRACT FROM AUTHOR]

Although previous research has identified ways in which environmental conditions influence population processes in many ecosystems, these relationships often weaken or break down with the inclusion of additional years of data, for reasons that remain unclear.To better understand if and how the relationships between historically important environmental drivers of yellow perch (Percaflavescens) recruitment varied during 1969–2018 in two Lake Erie basins characterised by contrasting productivity, we used dynamic linear modelling, which allows for nonstationary relationships between predictor and response variables to be quantified.Our analyses revealed that abiotic conditions during egg and larval development (i.e. winter temperature, springtime river inflows, and thermal conditions) were important in the productive west basin, whereas a combination of abiotic (i.e. winter temperature and river inflows) and biotic (i.e. small‐bodied and large‐bodied predators) conditions were important in the less‐productive central basin.While the importance of most environmental factors to yellow perch recruitment remained static through time, the importance of river inflows changed through time in the west and central basins. Large‐bodied predators (i.e. walleye, Sandervitreus) also varied in their importance through time in the central basin. These changes in recruitment drivers were independent of temporal changes to the environmental conditions themselves, coinciding instead with the timing of major shifts in Lake Erie's nutrient status.Our results demonstrate that ecosystem change can cause the relationships between environmental conditions and population processes (e.g. recruitment) of aquatic organisms to vary through time and space. They also highlight the value of periodically revisiting their applicability, especially if these relationships are used to help manage or sustain populations in ecosystems experiencing human‐driven environmental change. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unlike sales data, data on intermediate stages of the purchase funnel (e.g., how many consumers have searched for information about a product before purchase) are much more difficult to acquire. Consequently, most advertising response models have focused directly on sales and ignored other purchase funnel activities. The authors demonstrate, in the context of the U.S. automotive market, how consumer online search volume data from Google Trends can be combined with sales data to decompose advertising's overall impact into two underlying components: its impacts on (1) generating consumer interest in prepurchase information search and (2) converting that interest into sales. The authors show that this decompositional approach, implemented through a novel state-space model that simultaneously examines sales and search volumes, offers important advantages over a benchmark model that considers sales data alone. First, the approach improves goodness-of-fit, both in and out of sample. Second, it improves diagnosticity by distinguishing advertising effectiveness in interest generation from its effectiveness in interest conversion. Third, the authors find that overall advertising elasticity can be biased if researchers consider only sales data. [ABSTRACT FROM AUTHOR]

Consumer attitudes and behaviors are fundamentally dynamic processes; thus, understanding consumer dynamics is crucial for truly understanding consumer behaviors and for firms to formulate appropriate actions. Recent history in empirical marketing research has enjoyed increasingly richer consumer data as the result of technology and firms' conscious data collection efforts. Richer data, in turn, have propelled the development and application of quantitative methods in modeling consumer dynamics, and have contributed to the understanding of complex dynamic behaviors across many domains. In this paper, we discuss the sources of consumer dynamics and how our understanding in this area has improved over the past four decades. Accordingly, we discuss several commonly used empirical methods for conducting dynamics research. Finally, as the data evolution continues into new forms and new environments, we identify cutting-edge trends and domains, and offer directions for advancing the understanding of consumer dynamics in these emerging areas. [ABSTRACT FROM AUTHOR]

It has been established from previous studies that chlorophyll- a surface concentration has been declining in the eastern English Channel. This decline has been attributed to a decrease in nutrient concentrations in the rivers. However, the decrease in river discharge could also be a cause. In our study, rivers outflows and in-situ data have been compared to time series of satellite-derived chlorophyll- a concentrations. Dynamic Linear Model has been used to extract the dynamic and seasonally adjusted trends of several environmental variables. The results showed that, for the 1998–2019 period, chlorophyll- a levels stayed significantly lower than average and satellite images revealed a coast to offshore gradient. Chlorophyll- a concentration of coastal stations appeared to be related to the declining fluxes of phosphate while offshore stations were more related to nitrate-nitrite. Therefore, we can exclude that the climate variability, through river flows alone, has a dominant effect on the decline of chlorophyll- a concentration. • Chlorophyll declined during two decades in the Eastern English Channel. • Satellite images revealed coast to offshore chlorophyll decline gradient. • Changes in river flow were a minor driver of chlorophyll decline. • Phosphate was main explanatory variable for coastal stations chlorophyll decrease. • Nitrate-nitrite were related to offshore stations chlorophyll decrease. [ABSTRACT FROM AUTHOR]

This paper, "Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue," proposes a novel Bayesian approach based on dynamic linear models to share information from different sectors, LSG (Local Store Group), and item category, through the use of auxiliary information (the discount information). The authors demonstrate the feasibility of parallel computing with multiple item categories, making the Bayesian method highly scalable. The proposed method in the paper should have wide applicability in inventory and revenue management. We suggest in this discussion potential areas for further development. [ABSTRACT FROM AUTHOR]

With sea levels projected to rise as a result of climate change, it is imperative to understand not only long-term average trends, but also the spatial and temporal patterns of extreme sea level. In this study, we use a comprehensive set of 30 tide gauges spanning 1954–2014 to characterize the spatial and temporal variations of extreme sea level around the low-lying and densely populated margins of the South China Sea. We also explore the long-term evolution of extreme sea level by applying a dynamic linear model for the generalized extreme value distribution (DLM-GEV), which can be used for assessing the changes in extreme sea levels with time. Our results show that the sea-level maxima distributions range from ~ 90 to 400 cm and occur seasonally across the South China Sea. In general, the sea-level maxima at northern tide gauges are approximately 25–30% higher than those in the south and are highest in summer as tropical cyclone-induced surges dominate the northern signal. In contrast, the smaller signal in the south is dominated by monsoonal winds in the winter. The trends of extreme high percentiles of sea-level values are broadly consistent with the changes in mean sea level. The DLM-GEV model characterizes the interannual variability of extreme sea level, and hence, the 50-year return levels at most tide gauges. We find small but statistically significant correlations between extreme sea level and both the Pacific Decadal Oscillation and El Niño/Southern Oscillation. Our study provides new insight into the dynamic relationships between extreme sea level, mean sea level and the tidal cycle in the South China Sea, which can contribute to preparing for coastal risks at multi-decadal timescales. [ABSTRACT FROM AUTHOR]

Summary: We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global–local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we model dependence between the local scale parameters. The resulting processes inherit the desirable shrinkage behaviour of popular global–local priors, such as the horseshoe prior, but provide additional localized adaptivity, which is important for modelling time series data or regression functions with local features. We construct a computationally efficient Gibbs sampling algorithm based on a Pólya–gamma scale mixture representation of the process proposed. Using dynamic shrinkage processes, we develop a Bayesian trend filtering model that produces more accurate estimates and tighter posterior credible intervals than do competing methods, and we apply the model for irregular curve fitting of minute‐by‐minute Twitter central processor unit usage data. In addition, we develop an adaptive time varying parameter regression model to assess the efficacy of the Fama–French five‐factor asset pricing model with momentum added as a sixth factor. Our dynamic analysis of manufacturing and healthcare industry data shows that, with the exception of the market risk, no other risk factors are significant except for brief periods. [ABSTRACT FROM AUTHOR]

In this study, we develop a new methodology that extends multilevel structural equation model techniques by considering both temporal variation and unit-level random effects simultaneously for nested repeated cross-sectional samples. Our modeling approach essentially uses a state-space model form and a multilevel structural equation form to "marry" the time series and cross-sectional sources of variation. We apply the proposed model in a national-level customer satisfaction tracking study. Our empirical analysis explores the long-term effects of feature-level customer satisfaction on overall satisfaction. [ABSTRACT FROM AUTHOR]

Discovering temporal evolution of themes from a time-stamped collection of text poses a challenging statistical learning problem. Dynamic topic models offer a probabilistic modeling framework to decompose a corpus of text documents into "topics", i.e., probability distributions over vocabulary terms, while simultaneously learning the temporal dynamics of the relative prevalence of these topics. We extend the dynamic topic model of Blei and Lafferty (2006) by fusing its multinomial factor model on topics with dynamic linear models that account for time trends and seasonality in topic prevalence. A Markov chain Monte Carlo (MCMC) algorithm that utilizes P'olya-Gamma data augmentation is developed for posterior sampling. Conditional independencies in the model and sampling are made explicit, and our MCMC algorithm is parallelized where possible to allow for inference in large corpora. Our model and inference algorithm are validated with multiple synthetic examples, and we consider the applied problem of modeling trends in real estate listings from the housing website Zillow. We demonstrate in synthetic examples that sharing information across documents is critical for accurately estimating document-specific topic proportions. Analysis of the Zillow corpus demonstrates that the method is able to learn seasonal patterns and locally linear trends in topic prevalence. [ABSTRACT FROM AUTHOR]

The number of timely diagnoses based on condition monitoring data is increasing with the growing usage of monitoring systems. In most of the methods used in these systems, a pre-established fault detection threshold is needed, while there are no specific limit values or thresholds in many cases, especially when the machine is unique. Also, in most actual applications, due to the kind of process and harsh environment, the noise inherent in the observed process exhibits non-Gaussian characteristics, making it a challenging task for diagnostics based on condition monitoring (CM) data. Therefore, this paper introduced a robust methodology based on the switching maximum correntropy Kalman filter (SMCKF) to address the mentioned problems (threshold and online diagnostics in the presence of non-Gaussian noise by using CM data). This approach uses multiple dynamic system models to explain different degradation stages, utilizing robust Bayesian estimation. As this approach is based on dynamic behavior, a threshold for diagnostics is no longer needed. Ultimately, the proposed approach is applied to the online diagnosis of simulated and actual data sets. The results of both simulated and real data sets prove the method's efficacy. [ABSTRACT FROM AUTHOR]

We analyzed the vertical distribution of atmospheric methane (CH 4 ) retrieved from measurements by ground-based Fourier Transform Spectrometer (FTS) instrument in Sodankylä, Northern Finland. The retrieved dataset covers 2009−2018. We used a dimension reduction retrieval method to extract the profile information, since each measurement contains around three pieces of information about the profile shape between 0 and 40 km. We compared the retrieved profiles against Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) satellite measurements and AirCore balloon-borne profile measurements. Additional comparison at the lowest tropospheric layer was done against in-situ measurements from a 50-m-high mast. In general, the ground-based FTS and ACE-FTS profiles agreed within 10% below 20 km and within 30% in the stratosphere between 20 and 40 km. Our method was able to accurately capture reduced methane concentrations inside the polar vortex in the Arctic stratosphere. The method produced similar trend characteristics as the reference instruments even when a static prior profile was used. Finally, we analyzed the time series of the CH 4 profile datasets and estimated the trend using the dynamic linear model (DLM).

Highlights: • We have constructed a new tool for frequent growth monitoring in pigs. • The tool is able to produce growth alarms on individual and batch level. • We obtained similar batch alarms for identified and unidentified pigs. • The tool could be used to obtain economic value of frequent growth monitoring. Abstract In this paper we described a new tool used for frequent growth monitoring in pig production. The aim of the constructed tool was to alert farmers about deterioration in pigs' growth and to provide current and historical growth statistics for consecutive batches. The tool was built as a dynamic linear model (DLM) for systems with identified and unidentified pigs. The growth of pigs was described by parameters representing an initial body weight (BW), average daily gain and daily fluctuations in BW of pigs. Moreover, the constructed tool was built to account for increasing variation in BW of pigs over time as well as autocorrelation between BW measurements of observed individuals. The forecast errors obtained from the DLM were standardized and monitored with a tabular cusum. Alarms obtained from the cusum were registered both on batch (identified and unidentified pigs) and individual level (identified pigs). The constructed tool was tested on data gathered between February and September 2016. Altogether, data from 3 batches, 1058 individuals and 146,926 BW observations were collected and analyzed. For all 3 batches, filtered and smoothened growth means were obtained and plotted. Using the model for identified and unidentified pigs, the growth problems were detected on certain dates in Batch 2 and Batch 3. In both identification scenarios, similar batch alarms were obtained. The example on growth alarms for individual pigs (using version of the model for identified pigs) was also provided. The filtered herd mean, obtained from the constructed DLM, was calculated based on all pigs present at the start of fattening. Therefore, the obtained batch BW mean was accounting also for the heaviest individuals sold after exceeding a defined target weight from the observed batches. Hence, the presented tool can be useful in obtaining growth statistics for systems where pigs are sold continuously. Frequent BW information can be useful in informing farmers about unexpected events influencing growth e.g. outbreaks of diseases or management problems. Moreover, the historical information on growth might be valuable in making optimal decisions regarding management. In the future the economic value of information from frequent BW monitoring should be assessed. In this study, we have demonstrated that frequent observations of unidentified pigs can provide meaningful alarms on growth deterioration at the batch level. [ABSTRACT FROM AUTHOR]

Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system. [ABSTRACT FROM AUTHOR]

In many retail contexts, social interaction plays an important role in the shopping process. We propose a three-stage dynamic linear model that captures the influence of group discussion on shopper behavior within a hierarchical Bayes framework. The model is tested using a video tracking and transaction dataset from a specialty apparel store. The research reveals that group conversations have a significant impact on the shopper’s department or “zone” choice, purchase likelihood, and spending over time. This group influence is magnified by the size of the group (particularly for zone penetration and purchase conversion), and is also moderated by group composition and cohesiveness. The conversations of mixed-age groups and groups who stay together while shopping have a significant influence on shopper behavior across all three stages, while discussions by adult groups exhibit a marginal carryover effect for purchase conversion. When shoppers have repeated discussions in a specific department, they are more likely to return to and buy from this department, while the cumulative number of discussions in the store drives higher spending levels. We also observe that group shoppers visit more departments than their solo counterparts; and mixed-age groups and solo shoppers are more likely to buy than adults-only or teen groups. This study has important implications for how retailers manage shopper engagement and group interaction in their stores. [ABSTRACT FROM AUTHOR]

Missing data are prevalent issue in analyses involving data collection. The problem of missing data is exacerbated for multisource analysis, where data from multiple sensors are combined to arrive at a single conclusion. In this scenario, it is more likely to occur and can lead to discarding a large amount of data collected; however, the information from observed sensors can be leveraged to estimate those values not observed. We propose two methods for imputation of multisource data, both of which take advantage of potential correlation between data from different sensors, through ridge regression and a state‐space model. These methods, as well as the common median imputation, are applied to data collected from a variety of sensors monitoring an experimental facility. Performance of imputation methods is compared with the mean absolute deviation; however, rather than using this metric to solely rank the methods, we also propose an approach to identify significant differences. Imputation techniques will also be assessed by their ability to produce appropriate confidence intervals, through coverage and length, around the imputed values. Finally, performance of imputed datasets is compared with a marginalized dataset through a weighted k‐means clustering. In general, we found that imputation through a dynamic linear model tended to be the most accurate and to produce the most precise confidence intervals, and that imputing the missing values and down weighting them with respect to observed values in the analysis led to the most accurate performance. Published 2017. This article is a U.S. Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]