Flexible Purchase Frequency Modeling

1996 ◽  
Vol 33 (1) ◽  
pp. 94-107 ◽  
Author(s):  
Patrick L. Brockett ◽  
Linda L. Golden ◽  
Harry H. Panjer
Keyword(s):  
Panel Data ◽  
General Framework ◽  
Data Sets ◽  
Compound Poisson ◽  
Frequency Distributions ◽  
Purchase Frequency ◽  
Individual Level ◽  
Poisson Models ◽  
Marketing Models ◽  
Second Period

The authors present a general framework for purchase frequency modeling that enables flexible fitting and convenient computation. Their easily described purchase frequency distributions subsume many previous models and provide a connection between mixed Poisson marketing models and the conceptually distinct compound Poisson models. These distributions provide simple parametric equations for individual-level prediction of second-period purchase frequency based on observed first-period purchase frequencies. The results are applied to four marketing panel data sets.

scholarly journals Examining social determinants by locale to inform community linkages

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Davis
Keyword(s):  
Social Determinants ◽  
Low Income ◽  
Negative Impact ◽  
Rank Order ◽  
Disabled Persons ◽  
Behavioral Risk ◽  
Exploratory Research ◽  
Frequency Distributions ◽  
Individual Level ◽  
Negative Impacts

Abstract Achieving a long-lasting impact on health outcomes requires focus not just on patient care, but also on community approaches aimed at improving population health through addressing gaps in Social Determinants of Health (SDOH). SDOH have been found to disproportionately affect those in low-income brackets and the disabled to varying degrees based on locale. The purpose of this exploratory research was to determine 1) which SDOH have the greatest negative impact on disabled and elderly populations within four targeted states (Iowa, Ohio, Minnesota, Wisconsin) and 2) if there is a difference in negative SDOH impact between metro and non-metro locales. Individual-level data were obtained from disabled persons aged 65 years or older who responded to the Centers for Disease Control and Prevention's 2017 Behavioral Risk Factor Surveillance System (BRFSS) survey. Utilizing these data, frequency distributions were obtained using SPSS. Rank order variation in SDOH was observed among four Midwestern states and between metro vs. non-metro geographic regions. Frequency distributions assisted in identifying the greatest negative impacting SDOH on elderly disabled populations. An examination of the rank order tables allowed the investigator to accurately assess the rank of negative impacts. There were variabilities in responses to questions with moving two or more times within 12 months having the lowest negative impact. When regrouped based upon SDOH negative impacts, were you able to pay your bills was the most frequent SDOH across all states. Feeling unsafe or extremely unsafe in your neighborhood was the highest negatively impacted SDOH within states. Cited determinants in three categories were highest in Ohio. Ohio also had the highest proportion of negatively impacted SDOH across all states. No money for balanced meals was a close second SDOH across states. Key messages Social Determinants Impacting Elderly Disabled. Impact of Social Determinants by Geography.

scholarly journals Assessment of Sampling Effects on Various Satellite-Derived Integrated Water Vapor Datasets Using GPS Measurements in Germany as Reference

2020 ◽  
Vol 12 (7) ◽  
pp. 1170 ◽  
Author(s):  
Cintia Carbajal Henken ◽  
Lisa Dirks ◽  
Sandra Steinke ◽  
Hannes Diedrich ◽  
Thomas August ◽  
...  
Keyword(s):  
Water Vapor ◽  
Pairwise Comparison ◽  
Weather Conditions ◽  
Satellite System ◽  
High Temporal Resolution ◽  
Data Sets ◽  
Frequency Distributions ◽  
Temporal Sampling ◽  
Global Navigation Satellite ◽  
Satellite Instruments

Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high temporal resolution. Therefore, they serve as a reference to assess the quality and sampling issues of IWV products from multiple satellite instruments that show different orbital and instrument characteristics. A direct pairwise comparison between one year of IWV data from GPS and satellite instruments reveals overall biases (in kg/m 2 ) of 1.77, 1.36, 1.11, and −0.31 for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Computed monthly means show similar behaviors. No significant impact of averaging time and the low temporal sampling on aggregated satellite IWV data is found, mostly related to the noisy weather conditions in the German domain. In combination with SEVIRI cloud coverage, a change of shape of IWV frequency distributions towards a bi-modal distribution and loss of high IWV values are observed when limiting cases to daytime and clear sky. Overall, sampling affects mean IWV values only marginally, which are rather dominated by the overall retrieval bias, but can lead to significant changes in IWV frequency distributions.

scholarly journals Spatial Heterogeneity of the Incidence of Powdery Mildew on Hop Cones

Plant Disease ◽  
2006 ◽  
Vol 90 (11) ◽  
pp. 1433-1440 ◽  
Author(s):  
David H. Gent ◽  
Walter F. Mahaffee ◽  
William W. Turechek
Keyword(s):  
Powdery Mildew ◽  
Spatial Heterogeneity ◽  
Power Law ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Disease Assessment ◽  
Cluster Sampling ◽  
Data Sets ◽  
Ratio Test ◽  
Frequency Distributions

The spatial heterogeneity of the incidence of hop cones with powdery mildew (Podosphaera macularis) was characterized from transect surveys of 41 commercial hop yards in Oregon and Washington from 2000 to 2005. The proportion of sampled cones with powdery mildew ( p) was recorded for each of 221 transects, where N = 60 sampling units of n = 25 cones assessed in each transect according to a cluster sampling strategy. Disease incidence ranged from 0 to 0.92 among all yards and dates. The binomial and beta-binomial frequency distributions were fit to the N sampling units in a transect using maximum likelihood. The estimation procedure converged for 74% of the data sets where p > 0, and a loglikelihood ratio test indicated that the beta-binomial distribution provided a better fit to the data than the binomial distribution for 46% of the data sets, indicating an aggregated pattern of disease. Similarly, the C(α) test indicated that 54% could be described by the beta-binomial distribution. The heterogeneity parameter of the beta-binomial distribution, θ, a measure of variation among sampling units, ranged from 0.01 to 0.20, with a mean of 0.037 and a median of 0.015. Estimates of the index of dispersion ranged from 0.79 to 7.78, with a mean of 1.81 and a median of 1.37, and were significantly greater than 1 for 54% of the data sets. The binary power law provided an excellent fit to the data, with slope and intercept parameters significantly greater than 1, which indicated that heterogeneity varied systematically with the incidence of infected cones. A covariance analysis indicated that the geographic location (region) of the yards and the type of hop cultivar had little effect on heterogeneity; however, the year of sampling significantly influenced the intercept and slope parameters of the binary power law. Significant spatial autocorrelation was detected in only 11% of the data sets, with estimates of first-order autocorrelation, r1, ranging from -0.30 to 0.70, with a mean of 0.06 and a median of 0.04; however, correlation was detected in only 20 and 16% of the data sets by median and ordinary runs analysis, respectively. Together, these analyses suggest that the incidence of powdery mildew on cones was slightly aggregated among plants, but patterns of aggregation larger than the sampling unit were rare (20% or less of data sets). Knowledge of the heterogeneity of diseased cones was used to construct fixed sampling curves to precisely estimate the incidence of powdery mildew on cones at varying disease intensities. Use of the sampling curves developed in this research should help to improve sampling methods for disease assessment and management decisions.

Waking Up in the Poll Booth

2009 ◽  
Vol 7 (4) ◽  
pp. 901-910
Author(s):  
Robert E. Goodin ◽  
James Mahmud Rice
Keyword(s):  
United States ◽  
United Kingdom ◽  
Panel Data ◽  
The United States ◽  
Election Campaign ◽  
Individual Level ◽  
The Past ◽  
The United Kingdom ◽  
General Elections ◽  
Waking Up

Judging from Gallup Polls in the United States, the United Kingdom, and Australia, opinion often changes during an election campaign. Come election day itself, however, opinion often reverts back nearer to where it was before the campaign began. That that happens even in Australia, where voting is compulsory and turnout is near-universal, suggests that differential turnout among those who have and have not been influenced by the campaign is not the whole story. Inspection of individual-level panel data from 1987 and 2005 British General Elections confirms that between 3 and 5 percent of voters switch voting intentions during the campaign, only to switch back toward their original intentions on election day. One explanation, we suggest, is that people become more responsible when stepping into the poll booth: when voting they reflect back on the government's whole time in office, rather than just responding (as when talking to pollsters) to the noise of the past few days' campaigning. Inspection of Gallup Polls for UK snap elections suggests that this effect is even stronger in elections that were in that sense unanticipated.

scholarly journals The truth revisited: Bayesian analysis of individual differences in the truth effect

2020 ◽  
Author(s):  
Martin Schnuerch ◽  
Lena Nadarevic ◽  
Jeffrey Rouder
Keyword(s):  
Individual Differences ◽  
Model Comparison ◽  
Bayes Factor ◽  
Factor Model ◽  
Cognitive Task ◽  
Data Sets ◽  
Individual Level ◽  
Truth Effect ◽  
Negative Truth ◽  
Positive Truth

The repetition-induced truth effect refers to a phenomenon where people rate repeated statements as more likely true than novel statements. In this paper we document qualitative individual differences in the effect. While the overwhelming majority of participants display the usual positive truth effect, a minority are the opposite – they reliably discount the validity of repeated statements, what we refer to as negative truth effect. We examine 8 truth-effect data sets where individual-level data are curated. These sets are composed of 1,105 individuals performing 38,904 judgments. Through Bayes factor model comparison, we show that reliable negative truth effects occur in 5 of the 8 data sets. The negative truth effect is informative because it seems unreasonable that the mechanisms mediating the positive truth effect are the same that lead to a discounting of repeated statements' validity. Moreover, the presence of qualitative differences motivates a different type of analysis of individual differences based on ordinal (i.e., Which sign does the effect have?) rather than metric measures. To our knowledge, this paper reports the first such reliable qualitative differences in a cognitive task.

scholarly journals Detecting Bimodality in the Analogical Reasoning Performance of Elementary Schoolchildren

1997 ◽  
Vol 20 (3) ◽  
pp. 529-547 ◽  
Author(s):  
Bettina Hosenfeld ◽  
Han L.J. van der Maas ◽  
Dymphna C. van den Boom
Keyword(s):  
Repeated Measures ◽  
Analogical Reasoning ◽  
Test Session ◽  
Component Model ◽  
Control Group ◽  
Latent Classes ◽  
Data Sets ◽  
Data Set ◽  
Frequency Distributions ◽  
Group Data

This paper reports on modelling six frequency distributions representing the analogical reasoning performance of four different samples of elementary schoolchildren. A two-component model outperformed a one-component model in all investigated data sets, discriminating accurate performers with high success probabilities and inaccurate performers with low success probabilities, whereas for two data sets a three-component model provided the best fit. In a treatment-control group data set, the treatment group comprised a larger proportion of accurate performers than the control group, whereas the success probabilities of the two latent classes were nearly identical in both groups. In a repeated-measures data set, both the success probabilities of the two latent classes and the proportion of accurate performers increased from the first to the second test session. The results provided a first indication of a transition in the development of analogical reasoning in elementary schoolchildren.

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