Heteroscedastic Methods for Performing All Pairwise Comparisons of Regression Lines Associated With J Independent Groups

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 110-115 ◽  
Author(s):  
Rand R. Wilcox ◽  
Jinxia Ma
Keyword(s):  
Least Squares ◽  
Pairwise Comparisons ◽  
Simple Extension ◽  
Type I ◽  
Type I Errors ◽  
Well Elderly ◽  
Using Data

Abstract. The paper compares methods that allow both within group and between group heteroscedasticity when performing all pairwise comparisons of the least squares lines associated with J independent groups. The methods are based on simple extension of results derived by Johansen (1980) and Welch (1938) in conjunction with the HC3 and HC4 estimators. The probability of one or more Type I errors is controlled using the improvement on the Bonferroni method derived by Hochberg (1988) . Results are illustrated using data from the Well Elderly 2 study, which motivated this paper.

MicroRNA Expression Profiling of High and Low Risk MDS

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 3645-3645
Author(s):  
Joop Gaken ◽  
Azim M Mohamedali ◽  
Natalie Twine ◽  
Nigel Westwood ◽  
Barbara Czepulkowski ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Low Risk ◽  
Differentially Expressed ◽  
Type I ◽  
Type I Errors ◽  
Monosomy 7 ◽  
Cd34 Cells ◽  
Quantile Normalisation ◽  
Using Data ◽  
Normal Controls

Abstract MicroRNA’s (miR) are small noncoding RNA’s of 18–25 nucleotides that have a critical impact on gene regulation affecting cell growth & differentiation. Importantly, miR expression profiles can help distinguish normal cells from cancerous cells. This is particularly relevant to myelodysplastic syndromes both because of their heterogeneity as well as the difficulties associated with the early diagnosis of these disorders. We investigated the miR profiles of CD34+ and total nucleated cells (TNC) from different subtypes of MDS (n=135) and compared these to AML (n=20), normal CD34+ (n=5) and normal TNC’s (n=6). We used the Illumina universal array matrix to interrogate 470 validated miR’s from the Sanger mir-Base database and a further 265 putative miR’s from the literature. Array intensity data was analysed using the Partek Genomics Suite v6.3. Data was normalised using quantile normalisation and unchanging miR’s removed. A 1-way ANOVA was used to identify differentially expressed miR’s and a FDR correction applied to control for Type I errors. Our initial analysis compared TNC (n=6) with CD34 cells (n=5) from normal controls. As expected both groups clustered separately with mir-199a (p<10−9), mir-30a-3p (p<10−7), mir-507 (p<10−7) showing the most significant differences out of a total of 250 differentially expressed miR’s. Our results clearly identified an expression signature for low risk (LR), high risk (HR) and AML using data from both TNC’s and CD34+ cells compared to normal TNC and CD34+ cells. Cluster analysis of miR’s of CD34+ cells from normal controls (n=5) and monosomy 7 (−7) patients (n=6) identified a panel of 18 miR’s that differentiated both groups. Similarly, analysis of CD34+ cells from HR MDS (n=7) and normal CD34+ controls identified a unique panel of 34 miR’s differentiating both groups. Analysis of CD34+ cells from both del(5q) (n=4) and LR MDS cases (n=8) identified a panel of 154 and 23 miR’s respectively that differentiated these from the normal controls. A 4-way intersect analysis of miR expression from CD34+ cells from LR, HR, del(5q) and −7 groups did not identify any common miR’s when compared to normal CD34+ cells. However, a 3-way analysis using LR, HR and −7 groups identified mir-34a as a single common miR. The mir-34 family (a, b, c) are transcriptional targets for p53 and have been shown to be induced by DNA damage and oncogenic stress (He et al, 2007). A direct comparison of miR expression in CD34+ cells from LR and HR showed only 4 miR’s that are differentially expressed with significant fold changes; mir-656 (53x), mir-498 (32x), mir-181b (−17x) and mir-130a (−4x). Such differences where not observed in TNC from LR and HR samples. A 4-way intersect analysis on TNC’s from LR (n=47), del(5q) (n=5), HR (n=50) and −7 (n=8) cases identified 8 common miR’s; mir-34b, mir-139, mir-429, mir-519a, mir-548b, mir-561, mir-580 and mir-619. Comparison of the miR expression in TNC from AML samples against normal TNC identified 49 differentially expressed miR’s including let-7e, mir-181a, mir- 181b, mir-199a and mir-221 in accordance with previous published miR profiles in AML (Dixon-McIver et al, 2008). We also observed a 17 fold down regulation of mir-127 which is often silenced in cancer cells due to the methylation and acetylation status of the mir-127 promoter. The proto-oncogene BCL6 is a verified target of mir-127 (Saito et al, 2006). To our knowledge this is the first study in MDS that shows unique miR profiles in TNC for each subgroup when compared to normal TNC. Furthermore, miR profiles from CD34+ cells distinguish between the different MDS subtypes. In conclusion our analysis has shown that profiling miR’s in MDS results in unique profiles which may be used to discriminate between HR and LR MDS subgroups.

scholarly journals Identifying Which of J Independent Binomial Distributions Has the Largest Probability of Success

2020 ◽  
Vol 18 (2) ◽  
pp. 2-9
Author(s):  
Rand Wilcox
Keyword(s):  
Binomial Distribution ◽  
Random Variables ◽  
Independent Random Variables ◽  
Pairwise Comparisons ◽  
Type I ◽  
Type I Errors ◽  
Binomial Distributions ◽  
Probability Of Success

Let p1,…, pJ denote the probability of a success for J independent random variables having a binomial distribution and let p(1) ≤ … ≤ p(J) denote these probabilities written in ascending order. The goal is to make a decision about which group has the largest probability of a success, p(J). Let p̂1,…, p̂J denote estimates of p1,…,pJ, respectively. The strategy is to test J − 1 hypotheses comparing the group with the largest estimate to each of the J − 1 remaining groups. For each of these J − 1 hypotheses that are rejected, decide that the group corresponding to the largest estimate has the larger probability of success. This approach has a power advantage over simply performing all pairwise comparisons. However, the more obvious methods for controlling the probability of one more Type I errors perform poorly for the situation at hand. A method for dealing with this is described and illustrated.

Protecting the overall rate of Type I errors for pairwise comparisons with an omnibus test statistic.

1979 ◽  
Vol 86 (4) ◽  
pp. 884-888 ◽  
Author(s):  
Harvey J. Keselman ◽  
Paul A. Games ◽  
Joanne C. Rogan
Keyword(s):  
Pairwise Comparisons ◽  
Type I ◽  
Test Statistic ◽  
Omnibus Test ◽  
Type I Errors

Consequences of Going-Concern Opinion Inaccuracy at the Audit Office Level

2020 ◽  
Vol 39 (3) ◽  
pp. 185-208
Author(s):  
Qiao Xu ◽  
Rachana Kalelkar
Keyword(s):  
Market Share ◽  
Audit Quality ◽  
Type I ◽  
Type I Errors ◽  
Negatively Associated ◽  
Going Concern ◽  
Big 4 ◽  
Office Market ◽  
Going Concern Opinion ◽  
Office Level

SUMMARY This paper examines whether inaccurate going-concern opinions negatively affect the audit office's reputation. Assuming that clients perceive the incidence of going-concern opinion errors as a systematic audit quality concern within the entire audit office, we expect these inaccuracies to impact the audit office market share and dismissal rate. We find that going-concern opinion inaccuracy is negatively associated with the audit office market share and is positively associated with the audit office dismissal rate. Furthermore, we find that the decline in market share and the increase in dismissal rate are primarily associated with Type I errors. Additional analyses reveal that the negative consequence of going-concern opinion inaccuracy is lower for Big 4 audit offices. Finally, we find that the decrease in the audit office market share is explained by the distressed clients' reactions to Type I errors and audit offices' lack of ability to attract new clients.

scholarly journals Relaxing cosmological neutrino mass bounds with unstable neutrinos

2020 ◽  
Vol 2020 (12) ◽  
Author(s):  
Miguel Escudero ◽  
Jacobo Lopez-Pavon ◽  
Nuria Rius ◽  
Stefan Sandner
Keyword(s):  
Neutrino Mass ◽  
Particle Physics ◽  
Goldstone Boson ◽  
Mass Scale ◽  
Simple Extension ◽  
Type I ◽  
Mass Generation ◽  
Age Of The Universe ◽  
Neutrino Mass Models ◽  
The Right

Abstract At present, cosmological observations set the most stringent bound on the neutrino mass scale. Within the standard cosmological model (ΛCDM), the Planck collaboration reports ∑mv< 0.12 eV at 95 % CL. This bound, taken at face value, excludes many neutrino mass models. However, unstable neutrinos, with lifetimes shorter than the age of the universe τν ≲ tU, represent a particle physics avenue to relax this constraint. Motivated by this fact, we present a taxonomy of neutrino decay modes, categorizing them in terms of particle content and final decay products. Taking into account the relevant phenomenological bounds, our analysis shows that 2-body decaying neutrinos into BSM particles are a promising option to relax cosmological neutrino mass bounds. We then build a simple extension of the type I seesaw scenario by adding one sterile state ν4 and a Goldstone boson ϕ, in which νi→ ν4ϕ decays can loosen the neutrino mass bounds up to ∑mv ∼ 1 eV, without spoiling the light neutrino mass generation mechanism. Remarkably, this is possible for a large range of the right-handed neutrino masses, from the electroweak up to the GUT scale. We successfully implement this idea in the context of minimal neutrino mass models based on a U(1)μ−τ flavor symmetry, which are otherwise in tension with the current bound on ∑mv.

scholarly journals An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds

2018 ◽  
Vol 7 (10) ◽  
pp. 409 ◽  
Author(s):  
Youqiang Dong ◽  
Ximin Cui ◽  
Li Zhang ◽  
Haibin Ai
Keyword(s):  
Point Clouds ◽  
High Density ◽  
Type I ◽  
Densification Process ◽  
Type I Errors ◽  
Second Stage ◽  
Standard Variance ◽  
Triangular Irregular Network ◽  
Two Stages ◽  
Type Ii Errors

The progressive TIN (triangular irregular network) densification (PTD) filter algorithm is widely used for filtering point clouds. In the PTD algorithm, the iterative densification parameters become smaller over the entire process of filtering. This leads to the performance—especially the type I errors of the PTD algorithm—being poor for point clouds with high density and standard variance. Hence, an improved PTD filtering algorithm for point clouds with high density and variance is proposed in this paper. This improved PTD method divides the iterative densification process into two stages. In the first stage, the iterative densification process of the PTD algorithm is used, and the two densification parameters become smaller. When the density of points belonging to the TIN is higher than a certain value (in this paper, we define this density as the standard variance intervention density), the iterative densification process moves into the second stage. In the second stage, a new iterative densification strategy based on multi-scales is proposed, and the angle threshold becomes larger. The experimental results show that the improved PTD algorithm can effectively reduce the type I errors and total errors of the DIM point clouds by 7.53% and 4.09%, respectively, compared with the PTD algorithm. Although the type II errors increase slightly in our improved method, the wrongly added objective points have little effect on the accuracy of the generated DSM. In short, our improved PTD method perfects the classical PTD method and offers a better solution for filtering point clouds with high density and standard variance.

scholarly journals The Impact of Public Debt on the Economic Growth of Jordan: An Empirical Study (2000- 2015)

2017 ◽  
Vol 6 (2) ◽  
pp. 114 ◽  
Author(s):  
Tawfiq Ahmad Mousa ◽  
Abudallah. M. LShawareh
Keyword(s):  
Economic Growth ◽  
Empirical Study ◽  
Regression Model ◽  
Least Squares ◽  
Public Debt ◽  
Least Squares Method ◽  
Negative Impact ◽  
External Debt ◽  
Using Data ◽  
The Impact

In the last two decades, Jordan’s economy has been relied on public debt in order to enhance the economic growth. As such, an understanding  of the dynamics between public debt and economic growth is very important in addressing the obstacles to economic growth. The study investigates the impact of public debt on economic growth using data from 2000 to 2015. The study employs least squares method and regression model to capture the impact of public debt on economic growth. The results of the analysis indicate that there is a negative impact of total public debt, especially the external debt on economic growth. 

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