Skip to main content Accessibility help
×
  1. Home
  2. Browse subjects
  3. Statistics and Probability
  4. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

52383 results in Statistics and Probability

Page 283 of 2620

  • ADDRESSING IMBALANCED INSURANCE DATA THROUGH ZERO-INFLATED POISSON REGRESSION WITH BOOSTING

  • Simon C.K. Lee
  • Journal:
    ASTIN Bulletin: The Journal of the IAA / Volume 51 / Issue 1 / January 2021
    Published online by Cambridge University Press:
    17 December 2020, pp. 27-55
    Print publication:
    January 2021

  • A machine learning approach to zero-inflated Poisson (ZIP) regression is introduced to address common difficulty arising from imbalanced financial data. The suggested ZIP can be interpreted as an adaptive weight adjustment procedure that removes the need for post-modeling re-calibration and results in a substantial enhancement of predictive accuracy. Notwithstanding the increased complexity due to the expanded parameter set, we utilize a cyclic coordinate descent optimization to implement the ZIP regression, with adjustments made to address saddle points. We also study how various approaches alleviate the potential drawbacks of incomplete exposures in insurance applications. The procedure is tested on real-life data. We demonstrate a significant improvement in performance relative to other popular alternatives, which justifies our modeling techniques.

  • Extended-spectrum beta-lactamase Escherichia coli and Klebsiella pneumoniae urinary tract infections

  • P. Vachvanichsanong, E. B. McNeil, P. Dissaneewate
  • Journal:
    Epidemiology & Infection / Volume 149 / 2021
    Published online by Cambridge University Press:
    17 December 2020, e12
    • Article
      • You have access
      • Open access
    • PDF
    • HTML
    • Export citation

  • The prevalence of extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli and Klebsiella pneumoniae urinary tract infections (UTIs) is increasing worldwide. We investigated the prevalence, clinical findings, impact and risk factors of ESBL E. coli/K. pneumoniae UTI through a retrospective review of the medical records of children with UTI aged <15 years admitted to Prince of Songkla University Hospital, Thailand over 10 years (2004–2013). Thirty-seven boys and 46 girls had ESBL-positive isolates in 102 UTI episodes, compared with 85 boys and 103 girls with non-ESBL isolates in 222 UTI episodes. The age of presentation and gender were not significantly different between the two groups. The prevalence of ESBL rose between 2004 and 2008 before plateauing at around 30–40% per year, with a significant difference between first and recurrent UTI episodes of 27.3% and 46.5%, respectively (P = 0.003). Fever prior to UTI diagnosis was found in 78.4% of episodes in the non-ESBL group and 61.8% of episodes in the ESBL group (P = 0.003). Multivariate analysis indicated that children without fever (odds ratio (OR) 2.14, 95% confidence interval (CI) 1.23–3.74) and those with recurrent UTI (OR 2.67, 95% CI 1.37–5.19) were more likely to yield ESBL on culture. Congenital anomalies of the kidney and urinary tract were not linked to the presence of ESBL UTI. In conclusion, ESBL producers represented one-third of E. coli/K. pneumoniae UTI episodes but neither clinical condition nor imaging studies were predictive of ESBL infections. Recurrent UTI was the sole independent risk factor identified.

  • 5 - Linear Transformations and z-Scores

  • Russell T. Warne, Utah Valley University
  • Book:
    Statistics for the Social Sciences
    Published online:
    24 December 2020
    Print publication:
    17 December 2020, pp 104-127

  • Summary

    Chapter 5 teaches how data analysts can change the scale of a distribution by performing a linear transformation, which is the process of adding, subtracting, multiplying, or dividing the data by a constant. Adding and subtracting a constant will change the mean of a variable, but not its standard deviation or variance. Multiplying and dividing by a constant will change the mean, the standard deviation, and the variance of a dataset. A table shows students shows how linear transformations change the values of models of central tendency and variability. One special linear transformation is the z-score. All z-score values have a mean of 0 and a standard deviation of 1. Putting datasets on a common scale permits comparisons across different units. But linear transformations, like the z-score transformation, force the data to have the desired mean and standard deviation. Yet, they do not change the shape of the distribution – only its scale. Indeed, all scales are arbitrary, and scientists can use linear transformations to give their data any mean and standard deviation they choose.

  • 8 - One-Sample t-Tests

  • Russell T. Warne, Utah Valley University
  • Book:
    Statistics for the Social Sciences
    Published online:
    24 December 2020
    Print publication:
    17 December 2020, pp 183-219

  • Summary

    A one-sample t-test is an NHST procedure that is appropriate when a z-test cannot be performed because the population standard deviation is unknown. The one-sample t-test follows all of the eight steps of the z-test, but requires modifications to accommodate the unknown sample standard deviation. First, the formulas that used σy now use the estimated population standard deviation based on sample data instead. Second, degrees of freedom must be calculated. Finally, t-tests use a new probability distribution called a t-distribution.

    This chapter also explains more about p-values. First, when p is lower than α, the null hypothesis is always rejected. Second, when p is higher than α, the null hypothesis is always retained. Therefore, we can determine whether p is smaller or larger than α by determining whether the null hypothesis was retained or rejected for α. This chapter also discusses confidence intervals (CIs), which are a range of plausible values for a population parameter. CIs can vary in width, which the researcher chooses. The 95% CI width is most common in social science research. Finally, one-sample t-tests can be used to test the hypothesis that the sample mean is equal to any value (not the population mean).

Page 283 of 2620