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The application of a doubly Type-Ⅱ censoring scheme, where observations are censored at both the left and right ends, is often used in various fields including social science, psychology, and economics. However, the observed sample size under this censoring scheme may not be large enough to apply a likelihood-based approach due to the occurrence of censoring at both ends. To effectively respond to this difficulty, we propose a pivotal-based approach within a doubly Type-Ⅱ censoring framework, focusing on two key aspects: Estimation for parameters of interest and prediction for missing or censored samples. The proposed approach offers two prominent advantages, compared to the likelihood-based approach. First, this approach leads to exact confidence intervals for unknown parameters. Second, it addresses prediction problems in a closed-form manner, ensuring computational efficiency. Moreover, novel algorithms using a pseudorandom sequence, which are introduced to implement the proposed approach, have remarkable scalability. The superiority and applicability of the proposed approach are substantiated in Monte Carlo simulations and real-world case analysis through a comparison with the likelihood-based approach.

This paper proposes an inference approach based on a pivotal quantity under the adaptive progressive Type-II censoring scheme. To exemplify the proposed methodology, an extensively employed distribution, a Pareto distribution, is utilized. This distribution has limitations in estimating confidence intervals for unknown parameters from classical methods such as the maximum likelihood and bootstrap methods. For example, in the maximum likelihood method, the asymptotic variance-covariance matrix does not always exist. In addition, both classical methods can yield confidence intervals that do not satisfy nominal levels when a sample size is not large enough. Our approach resolves these limitations by allowing us to construct exact intervals for unknown parameters with computational simplicity. Aside from this, the proposed approach leads to closed-form estimators with properties such as unbiasedness and consistency. To verify the validity of the proposed methodology, two approaches, a Monte Carlo simulation and a real-world data analysis, are conducted. The simulation testifies to the superior performance of the proposed methodology as compared to the maximum likelihood method, and the real-world data analysis examines the applicability and scalability of the proposed methodology.

Kaempferia parviflora (KP) rhizome, also called black ginger, has been used as a herbal medicine for many centuries. This current study was aimed at exploring whether KP rhizome extract (KPE) had anti-obesity effects and the mechanism involved. Five-week-old C57BL/6N male mice were allocated into five groups for 8-week feeding with control diet (CD), high-fat diet (HFD), HFD + 150 mg/kg body weight (BW)/day KPE (HFD+K150), HFD + 300 mg/kg BW/day KPE (HFD+K300), and HFD + 600 mg/kg BW/day KPE (HFD+K600). KPE decreased BW, body fat mass, adipose tissue weight, adipocyte size, and serum levels of glucose, triglycerides, cholesterol, insulin, and leptin in HFD-induced obese C57BL/6N mice. KPE inhibited adipogenesis by decreasing CCAAT/enhancer binding protein α, peroxisome proliferator-activated receptor γ, sterol regulatory element-binding protein-1c, acetyl-CoA carboxylase 1, ATP-citrate lyase, and fatty acid synthase mRNA expression. KPE improved lipolysis by increasing carnitine palmitoyl transferase 1 and hormone-sensitive lipase mRNA expression. These results suggest that KPE may have inhibited HFD-induced obesity by regulating several pathways involved in decreasing adipogenesis and enhancing lipolysis. Thus, the results suggest that KPE (or KP) may be applicable as an anti-obesity agent.

The present study was designed to explore whether Cydonia oblonga Miller extract (COME) had anti-obesity effects and the mechanism involved. COME reduced body weight, fat mass, adipose tissue weight, and serum levels of insulin, triglyceride, and leptin, but increased the serum levels of adiponectin and high-density lipoprotein-cholesterol in high-fat diet (HFD)-induced obese C57BL/6 mice. COME increased AMPK activation. COME suppressed adipogenesis by decreasing CCAAT/enhancer binding protein α, peroxisome proliferator-activated receptor γ, sterol regulatory element-binding protein-1c, acetyl-CoA carboxylase 1, ATP-citrate lyase, fatty acid synthase, and adipocyte protein 2 mRNA expression and enhanced lipolysis by increasing carnitine palmitoyl transferase 1 and hormone sensitive lipase mRNA expression. These results suggest that COME is potentially beneficial in inhibiting HFD-induced obesity by inducing AMPK activation and thereby, regulating several pathways involved in inhibiting adipogenesis, and promoting lipolysis and fatty acid oxidation. Our results suggest that COM may be used as an anti-obesity agent.

We derive some estimators of the scale parameter of the Rayleigh distribution under the unified hybrid censoring scheme. We also derive some estimators of the reliability function and the entropy of the Rayleigh distribution. First, we obtain the maximum likelihood estimator of the scale parameter. Second, we obtain the Bayes estimator using the mean of the posterior distribution. Lastly, we obtain the Bayes estimator using the mode of the posterior distribution. We also derive the interval estimation (confidence interval, credible interval, and HPD credible interval) for the scale parameter under the unified hybrid censoring scheme. We compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation. Coverage probability and average lengths of 95 % and 90% intervals are obtained.

This paper proposes a new approach based on the regression framework employing a pivotal quantity to estimate unknown parameters of a Weibull distribution under the progressive Type-II censoring scheme, which provides a closed form solution for the shape parameter, unlike its maximum likelihood estimator counterpart. To resolve serious rounding errors for the exact mean and variance of the pivotal quantity, two different types of Taylor series expansion are applied, and the resulting performance is enhanced in terms of the mean square error and bias obtained through the Monte Carlo simulation. Finally, an actual application example, including a simple goodness-of-fit analysis of the actual test data based on the pivotal quantity, proves the feasibility and applicability of the proposed approach.

This paper provides a new estimation equation based on the concept of a minimum distance between the empirical and theoretical distribution functions under the most widely used progressive Type-II censoring scheme. For illustrative purposes, simulated and real datasets from a three-parameter Weibull distribution are analyzed. For comparison, the most popular estimation methods, the maximum likelihood and maximum product of spacings estimation methods, are developed together. In the analysis of simulated datasets, the excellence of the provided estimation method is demonstrated through the degree of the estimation failure of the likelihood-based method, and its validity is demonstrated through the mean squared errors and biases of the estimators obtained from the provided estimation equation. In the analysis of the real dataset, two types of goodness-of-fit tests are performed on whether the observed dataset has the three-parameter Weibull distribution under the progressive Type-II censoring scheme, through which the performance of the new estimation equation provided is examined. [ABSTRACT FROM AUTHOR]

This paper proposes a new estimation method based on the maximum product of spacings for estimating unknown parameters of the three-parameter Weibull distribution under a generalized Type-II progressive hybrid censoring scheme which guarantees a constant number of observations and an appropriate experiment duration. The proposed approach is appropriate for a situation where the maximum likelihood estimation is invalid, especially, when the shape parameter is less than unity. Furthermore, it presents the enhanced performance in terms of the bias through the Monte Carlo simulation. In particular, the superiority of this approach is revealed even under the condition where the maximum likelihood estimation satisfies the classical asymptotic properties. Finally, to illustrate the practical application of the proposed approach, the real data analysis is conducted, and the superiority of the proposed method is demonstrated through a simple goodness-of-fit test. [ABSTRACT FROM AUTHOR]

The multiply Type-II hybrid censoring scheme is disadvantaged by an experiment time that is too long. To overcome this limitation, we propose a generalized multiply Type-II hybrid censoring scheme. Some estimators of the scale parameter of the exponential distribution are derived under a generalized multiply Type-II hybrid censoring scheme. First, the maximum likelihood estimator of the scale parameter of the exponential distribution is obtained under the proposed censoring scheme. Second, we obtain the Bayes estimators under different loss functions with a noninformative prior and an informative prior. We approximate the Bayes estimators by Lindleys approximation and the Tierney-Kadane method since the posterior distributions obtained by the two priors are complicated. In addition, the Bayes estimators are obtained by using the Markov Chain Monte Carlo samples. Finally, all proposed estimators are compared in the sense of the mean squared error through the Monte Carlo simulation and applied to real data. [ABSTRACT FROM AUTHOR]

In this paper, we derive some estimators of the scale parameter of the exponentiated half-logistic distribution based on the multiply Type-I hybrid censoring scheme. We assume that the shape parameter λ is known. We obtain the maximum likelihood estimator of the scale parameter σ. The scale parameter is estimated by approximating the given likelihood function using two different Taylor series expansions since the likelihood equation is not explicitly solved. We also obtain Bayes estimators using prior distribution. To obtain the Bayes estimators, we use the squared error loss function and general entropy loss function (shape parameter q = -0.5, 1.0). We also derive interval estimation such as the asymptotic confidence interval, the credible interval, and the highest posterior density interval. Finally, we compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation. The average length of 95% intervals and the corresponding coverage probability are also obtained. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate the prognostic value of each component of the revised American Fertility Society (rAFS) classification system for the first recurrence of endometriosis after conservative laparoscopy. Materials and Methods: As this was a retrospective cohort study, data were collected by reviewing medical records. A total of 379 women ages 18 to 49 years were included. Women who underwent conservative laparoscopy with histologic confirmation of endometriosis at Gangnam Severance Hospital between March 2003 and May 2010 were included. Individual components of the rAFS classification system as well as preoperative serum CA-125 levels were retrospectively analyzed to assess their prognostic values for recurrence of endometriosis. Results: Of 379 patients, 80 (21.2%) were found to have recurrence of endometriosis. The median duration of follow-up was 19.0 months, and the mean age at the time of surgery was 31.8±6.7 years. In endometriosis of advanced stage, younger age at the time of surgery, bilateral ovarian cysts at the time of diagnosis, a rAFS ovarian adhesion score >24, and complete cul-de-sac obliteration were independent risk factors of poor outcomes, and a rAFS ovarian adhesion score >24 had the highest risk of recurrence [hazard ratio=2.948 (95% CI: 1.116?7.789), p=0.029]. Conclusion: Our results suggest that of the rAFS adnexal adhesion scores, the ovarian adhesion score rather than the tubal adhesion score was associated with a significantly increased risk of recurrent endometriosis. The preoperative serum CA-125 level may be also a significant prognostic factor for recurrence, as known. However, it seemed to only have borderline significance in affecting recurrence in the current study. [ABSTRACT FROM AUTHOR]

The efficacy of Aster sphathulifolius (AS) for evaluation of preventive potencies against diabetes and diabetic complications was analyzed by assessment of inhibitory effects against advanced glycation end-products (AGEs) formation, rat lens aldose reductase (RLAR), α-glucosidase activity, and scavenging effects against DPPH. The 50% ethanol extract showed the highest DPPH radical scavenging activity (82.38%) at a concentration of 100 μg/ mL, exhibited the most potent AGEs formation inhibitory effect (93.88%) at a concentration of 200 μg/mL, showed the most potent RLAR inhibitory effect (86.44%) at a concentration of 11.11 μg/mL. The 95% ethanol extract showed a potent α-glucosidase inhibitory activity (19.16%) at a concentration of 1 mg/mL. The 50% ethanol extract value was 16.79%. The 50% ethanol extract exhibited the most potent antioxidant and preventive actions against diabetes and diabetic complications and can, thus, be a source of valuable compounds for prevention of diabetes and diabetic complications. [ABSTRACT FROM AUTHOR]

Background: Cartilage oligomeric matrix protein (COMP) is a biomarker for joint destruction and its serum levels are used for assessing therapeutic efficacy. This study aims to compare changes in serum COMP levels in postmenopausal women with osteopenia/osteoporosis receiving estrogen and alendronate. Methods: A total of 62 postmenopausal women diagnosed with osteopenia or osteoporosis were treated with either estrogen (17β-estradiol 1 mg, n = 30) or bisphosphonate (alendronate 5 rag, n = 32) for 6 months. The controls were healthy postmenopausal women (n = 30). Serum COMP and osteocalcin levels were measured at baseline and after 6 months of treatment. Results: Estrogen decreased levels of COMP at 6 months compared to baseline levels (-8.35 ± 19.38%), whereas the bisphosphonate and control groups resulted in no significant changes (-5.50 ± 18.69 and -1.49 ± 25.34%, respectively). Concentrations of osteocalcin decreased significantly in both treatment groups (estrogen -25.60 ± 24.42% and alendronate -13.76 ± 23.89%, respectively). There was a significant positive correlation between changes after 6 months in COMP and osteocalcin (R = 0.48, p = 0.002). Conclusions: Postmenopausal women treated with estrogen showed significantly decreased levels of COMP after 6 months. Estrogen might provide a further treatment modality in the prevention of joint destruction. [ABSTRACT FROM AUTHOR]