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Multicollinearity, a critical issue in regression analysis that can severely compromise the stability and accuracy of parameter estimates, arises when two or more variables exhibit correlation with each other. This paper solves this problem by introducing six new, improved two-parameter ridge estimators (ITPRE): NATPR1, NATPR2, NATPR3, NATPR4, NATPR5, and NATPR6. These ITPRE are designed to remove multicollinearity and improve the accuracy of estimates. A comprehensive Monte Carlo simulation analysis using the mean squared error (MSE) criterion demonstrates that all proposed estimators effectively mitigate the effects of multicollinearity. Among these, the NATPR2 estimator consistently achieves the lowest estimated MSE, outperforming existing ridge estimators in the literature. Application of these estimators to a real-world dataset further validates their effectiveness in addressing multicollinearity, underscoring their robustness and practical relevance in improving the reliability of regression models. [ABSTRACT FROM AUTHOR]

AbstractRegression analysis is widely used to predict the response variable utilizing one or more predictor variables. In many fields of study, the predictors are highly correlated causing multicollinearity problem that severely affects the efficiency of ordinary least square (OLS) estimators by significantly inflating their variances. To solve the multicollinearity problem, various one and two parameter ridge estimators are available in literature. In this article, a class of modified two parameter Lipovetsky–Conklin ridge estimators is proposed based on eigen values of X′X matrix that provide an automatic dealing option for treating different levels of multicollinearity. An extensive simulations study followed by real life example is used to evaluate the performance of proposed estimators based on MSE criterion. In most of the simulation conditions, our proposed estimators outperformed the existing estimators. [ABSTRACT FROM AUTHOR]

The inability of ordinary least square estimators against multicollinearity has paved the way for the development of various ridge-type estimators, which are recently classified as one-parameter and two-parameter ridge estimators. In this paper, we offer some efficient two-parameter ridge estimators and evaluate their performance through a simulation study by using the minimum mean square error criterion. Under most of the simulation conditions, our proposed estimators outperformed the existing estimators. Finally, two real-life datasets are used to demonstrate the applications of our proposed estimators. [ABSTRACT FROM AUTHOR]

Eight Jersey sired calves from Achai dams (AJC) and eleven Azakheli buffalo calves (ABC) were reared on milk replacer to compare their growth performance and economics. The experiment was performed at Livestock Research and Development Station (LR&DS), Surezai, Peshawar for a period of 12 weeks. The calves were fed with milk replacer (10% of their live body weight) in two equal doses, one in the morning followed by an evening dose, until the end of 8th week. The average birth weight of AJC was 15.75±2.43 kg; while, it was 28.18±0.87 kg for ABC calves. The average weaning weight was 43.56±4.3 kg and 56.5±2.75 kg for AJC and ABC calves, respectively. Whereas, the average weight gains in AJC and ABC calves was 0.331 kg/d and 0.337 kg/d, respectively. Average milk replacer consumed by AJC and ABC was 152.33 L/calf and 225.53 L/calf, respectively. Thus, the milk conversion ratio in ABC calves was significantly higher (p<0.01) than the AJC with 7.99 milk conversion ratio in ABC as compared to 5.49 for AJC calves. Additionally, the cost of production was Rs.406.68/kg and Rs.591.32/kg for AJC and ABC, respectively. This study concludes that AJC calves performed better on milk replacer than ABC. [ABSTRACT FROM AUTHOR]

A breeding trial under controlled farm conditions was carried out to estimate serum Revised: November 17, 2013 estrogen and progesterone spectrum over the entire gestation length in Kari ewes Accepted: June 03, 2014 belonging to three subtypes. Kari is an exceptional breed within ovine species that Key words: gestates in variable length of period (85-154 days). Ewes were bred through hand Estrogen mating, using rams from respective subtype based on the history of pregnancy Gestation duration (A:<110, B:111-130 and C:131-150 days). Upon lambing, the gestation Hormone length (GL) of ewes within each subtype was found at variance. Blood-serum Kari sampling was carried out weekly, commencing after mating and continued until Progesterone lambing. Samples collected from open ewes were discarded upon pregnancy Sheep confirmation examined through ultrasonography. The serum was analyzed for progesterone and estrogen profiles. The maximum progesterone concentration (24.2 ng/ml) observed in group C, during the 19th week of its gestation. Advancement in gestation boosted the peripheral blood progesterone concentration but in an undulating pattern. Serum progesterone at the start of pregnancy was at lower (2.1 ng/ml), reached at its peak (24.2 ng/ml) towards the end of gestation and dropped to (12.2 ng/ml) right after lambing. Maximum estrogen level (43.86 ng/ml) was observed on in group C, followed by B. Group A had the lower estrogen level with a minimal undulation throughout the gestation length. Estrogen was found fluctuating across the entire gestation period in ewes irrespective of their gestation length, which surged up towards the pregnancy termination. [ABSTRACT FROM AUTHOR]