Lifetime performance evaluation model based on quick response thinking

In practice, lifetime performance index CL has been a method commonly applied to the evaluation of quality performance. L is the upper or lower limit of the specification. The product lifetime distribution is mostly abnormal distribution. This study explored that the lifetime of commodities comes from exponential distribution. Complete data collection is the primary goal of analysis. However, the censoring type is one of the most commonly used methods due to considerations of manpower and material cost or the timeliness of product launch. This study adopted Type-II right censoring to find out the uniformly minimum variance unbiased (UMVU) estimator of the lifetime performance index CL and its probability density function. Afterward this study obtained the 100×(1-α)% confidence interval of the lifetime performance index CL as well as created the uniformly most powerful (UMP) test and the power of the test for the product lifetime performance index. Last, this study came up with a numerical example to demonstrate the suggested method as well as the application of the model.

A Comparison of Efficiency for Homogeneity of Variance Tests under Log-normal Distribution

In this paper, we compare five homogeneity of variance tests which are Bartlett’s test, Levene’s test, Cochran’s test, O’Brien’s test and Jackknife test. Considering their ability to control probability of type I error and the power of the test, when groups of population are distributed in log-normal distribution. The equal sample sizes are defined as 10, 15, 30 and 50 at the significance is 0.05. The results show that the Levene’s test become the best test for the high skewed distribution. For the lowed skew distribution, the Cochran’s test is the best test when a variance of group is different to the others. Moreover, Bartlett’s test provides the highest power when variances of all populations are different.

Association between Suicide and Toxoplasma gondii Seropositivity

This study aimed to determine the association between suicide and Toxoplasma gondii (T. gondii) seropositivity. Serum samples of 89 decedents who committed suicide (cases) and 58 decedents who did not commit suicide (controls) were tested for anti-T. gondii IgG and IgM antibodies using enzyme-linked immunosorbent assays. Anti-T. gondii IgM antibodies were further detected by enzyme-linked fluorescence assay (ELFA). A total of 8 (9.0%) of the 89 cases and 6 (10.3%) of the 58 controls were positive for anti-T. gondii IgG antibodies (OR: 0.85; 95% CI: 0.28–2.60; p = 0.78). Anti-T. gondii IgG levels were higher than 150 IU/mL in two (2.2%) cases and in five (8.6%) controls (OR: 0.24; 95% CI: 0.04–1.30; p = 0.11). Anti-T. gondii IgM antibodies were not found in any case or control using the enzyme immunoassay and were found in only one (1.7%) control using ELFA (p = 0.39). Rates of IgG seropositivity and high levels of anti-T. gondii antibodies were similar in cases and in controls regardless of their sex or age groups. The results do not support an association between T. gondii seropositivity and suicide. However, the statistical power of the test was low. Further research is necessary to confirm this lack of association.

Spatio-temporal modelling for non-stationary point referenced data

<p><b>Spatial and spatio-temporal phenomena are commonly modelled as Gaussian processes via the geostatistical model (Gelfand & Banerjee, 2017). In the geostatistical model the spatial dependence structure is modelled using covariance functions. Most commonly, the covariance functions impose an assumption of spatial stationarity on the process. That means the covariance between observations at particular locations depends only on the distance between the locations (Banerjee et al., 2014). It has been widely recognized that most, if not all, processes manifest spatially nonstationary covariance structure Sampson (2014). If the study domain is small in area or there is not enough data to justify more complicated nonstationary approaches, then stationarity may be assumed for the sake of mathematical convenience (Fouedjio, 2017). However, relationships between variables can vary significantly over space, and a ‘global’ estimate of the relationships may obscure interesting geographical phenomena (Brunsdon et al., 1996; Fouedjio, 2017; Sampson & Guttorp, 1992). </b></p> <p>In this thesis, we considered three non-parametric approaches to flexibly account for non-stationarity in both spatial and spatio-temporal processes. First, we proposed partitioning the spatial domain into sub-regions using the K-means clustering algorithm based on a set of appropriate geographic features. This allowed for fitting separate stationary covariance functions to the smaller sub-regions to account for local differences in covariance across the study region. Secondly, we extended the concept of covariance network regression to model the covariance matrix of both spatial and spatio-temporal processes. The resulting covariance estimates were found to be more flexible in accounting for spatial autocorrelation than standard stationary approaches. The third approach involved geographic random forest methodology using a neighbourhood structure for each location constructed through clustering. We found that clustering based on geographic measures such as longitude and latitude ensured that observations that were too far away to have any influence on the observations near the locations where a local random forest was fitted were not selected to form the neighbourhood. </p> <p>In addition to developing flexible methods to account for non-stationarity, we developed a pivotal discrepancy measure approach for goodness-of-fit testing of spatio-temporal geostatistical models. We found that partitioning the pivotal discrepancy measures increased the power of the test.</p>

Spatio-temporal modelling for non-stationary point referenced data

<p><b>Spatial and spatio-temporal phenomena are commonly modelled as Gaussian processes via the geostatistical model (Gelfand & Banerjee, 2017). In the geostatistical model the spatial dependence structure is modelled using covariance functions. Most commonly, the covariance functions impose an assumption of spatial stationarity on the process. That means the covariance between observations at particular locations depends only on the distance between the locations (Banerjee et al., 2014). It has been widely recognized that most, if not all, processes manifest spatially nonstationary covariance structure Sampson (2014). If the study domain is small in area or there is not enough data to justify more complicated nonstationary approaches, then stationarity may be assumed for the sake of mathematical convenience (Fouedjio, 2017). However, relationships between variables can vary significantly over space, and a ‘global’ estimate of the relationships may obscure interesting geographical phenomena (Brunsdon et al., 1996; Fouedjio, 2017; Sampson & Guttorp, 1992). </b></p> <p>In this thesis, we considered three non-parametric approaches to flexibly account for non-stationarity in both spatial and spatio-temporal processes. First, we proposed partitioning the spatial domain into sub-regions using the K-means clustering algorithm based on a set of appropriate geographic features. This allowed for fitting separate stationary covariance functions to the smaller sub-regions to account for local differences in covariance across the study region. Secondly, we extended the concept of covariance network regression to model the covariance matrix of both spatial and spatio-temporal processes. The resulting covariance estimates were found to be more flexible in accounting for spatial autocorrelation than standard stationary approaches. The third approach involved geographic random forest methodology using a neighbourhood structure for each location constructed through clustering. We found that clustering based on geographic measures such as longitude and latitude ensured that observations that were too far away to have any influence on the observations near the locations where a local random forest was fitted were not selected to form the neighbourhood. </p> <p>In addition to developing flexible methods to account for non-stationarity, we developed a pivotal discrepancy measure approach for goodness-of-fit testing of spatio-temporal geostatistical models. We found that partitioning the pivotal discrepancy measures increased the power of the test.</p>

CONSTRUCTION AND STANDARDIZATION OF LIFE SATISFACTION SCALE FOR COLLEGE OF TEACHER EDUCATORS

Conceptual and research based literature related to life satisfaction scale construction and educational psychology topics were studied thoroughly for developing life satisfaction scale n Educational Psychology. The preparation and standardization of the scale consisted of four major phases such as planning, construction, evaluation and validation. In present investigation fifty items/statements were prepared by the researcher which was reviewed by experts in the field and then first draft of the life satisfaction scale was ready for tryout. For pilot testing, the test was administered on representative sample of 50 college of teacher educators of different institutions keeping in mind that they should have knowledge of life satisfaction content and they must have gone through the content earlier. Achievement test having 50 items with three alternative choices each was given to participants and scoring was done with the help of scoring key. Difficulty Value and Discrimination Power of the test calculated. This test has a value 0.936 (Cronbach Alpha) for test consistency. Researcher also used different method to establish the reliability of the test.

Treatment With a Probiotic Mixture Containing Bifidobacterium Animalis Subsp. Lactis BB12 and Enterococcus Faecium l3 for the Prevention of Allergic Rhinitis in Children: A Randomized Controlled Trial

BACKGROUND: Probiotics may prevent the allergic response&rsquo;s development due to their anti-inflammatory and immunomodulatory effects. The aim of this study is to determine if the prophylactic treatment with a mixture of Bifidobacterium animalis subsp. Lactis BB12 and Enterococcus faecium L3, would reduce symptoms and need for drug use in children with allergic rhinitis (AR). METHODS: The study included 250 children aged from 6 to 17 years, affected by AR. Patients were randomly assigned to the intervention group (117) or to the placebo group (86). Patients of the intervention group, in addition to conventional therapy (local corticosteroids and/or antihistamines), were treated, in the 3 months preceding the development of AR symptoms, with a daily oral administration of a probiotic mixture containing the Bifidobacterium animalis subsp Lactis BB12 DSM 15954 and the Enterococcus faecium L3 LMG P-27496 strain. Nasal Symptoms Score(NSS) was used to evaluate AR severity before and after the treatment with probiotics or placebo. RESULTS: 96% of the patients in the intervention group showed a significant decrease in their NSS after the probiotic treatment as well as a decrease in the intake of pharmacological therapy. GPower software was used to calculate the test power. Given the probability of error &alpha; = 0.05, the total sample size n = 117 and the effect size &rho; = 2.0651316, the power of the test is 1 - &beta; = 1. CONCLUSIONS: When administered as a prophylactic treatment the mixture of BB12 and L3 statistically decrease signs and symptoms of AR and reduces significantly the need of drugs.

Effects of the prewhitening method, the time granularity, and the time segmentation on the Mann–Kendall trend detection and the associated Sen's slope

The Mann–Kendall test associated with the Sen's slope is a very widely used non-parametric method for trend analysis. It requires serially uncorrelated time series, yet most of the atmospheric processes exhibit positive autocorrelation. Several prewhitening methods have therefore been designed to overcome the presence of lag-1 autocorrelation. These include a prewhitening, a detrending and/or a correction of the detrended slope and the original variance of the time series. The choice of which prewhitening method and temporal segmentation to apply has consequences for the statistical significance, the value of the slope and of the confidence limits. Here, the effects of various prewhitening methods are analyzed for seven time series comprising in situ aerosol measurements (scattering coefficient, absorption coefficient, number concentration and aerosol optical depth), Raman lidar water vapor mixing ratio, as well as tropopause and zero-degree temperature levels measured by radio-sounding. These time series are characterized by a broad variety of distributions, ranges and lag-1 autocorrelation values and vary in length between 10 and 60 years. A common way to work around the autocorrelation problem is to decrease it by averaging the data over longer time intervals than in the original time series. Thus, the second focus of this study evaluates the effect of time granularity on long-term trend analysis. Finally, a new algorithm involving three prewhitening methods is proposed in order to maximize the power of the test, to minimize the number of erroneous detected trends in the absence of a real trend and to ensure the best slope estimate for the considered length of the time series.

Testing of Multifractional Brownian Motion

Fractional Brownian motion (FBM) is a generalization of the classical Brownian motion. Most of its statistical properties are characterized by the self-similarity (Hurst) index 0<H<1. In nature one often observes changes in the dynamics of a system over time. For example, this is true in single-particle tracking experiments where a transient behavior is revealed. The stationarity of increments of FBM restricts substantially its applicability to model such phenomena. Several generalizations of FBM have been proposed in the literature. One of these is called multifractional Brownian motion (MFBM) where the Hurst index becomes a function of time. In this paper, we introduce a rigorous statistical test on MFBM based on its covariance function. We consider three examples of the functions of the Hurst parameter: linear, logistic, and periodic. We study the power of the test for alternatives being MFBMs with different linear, logistic, and periodic Hurst exponent functions by utilizing Monte Carlo simulations. We also analyze mean-squared displacement (MSD) for the three cases of MFBM by comparing the ensemble average MSD and ensemble average time average MSD, which is related to the notion of ergodicity breaking. We believe that the presented results will be helpful in the analysis of various anomalous diffusion phenomena.